DAIR Lab | Publications

An up to date list of publications can also be found on Google scholar. When available, the links for each paper will also point to video highlights, code repositories, and presentation recordings.

Preprints

Vysics: Object Reconstruction Under Occlusion by Fusing Vision and Contact-Rich Physics

Bibit* Bianchini, Minghan* Zhu, Mengti Sun, Bowen Jiang, Camillo J. Taylor, and Michael Posa

In Robotics: Science and Systems (RSS), 2025

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We introduce Vysics, a vision-and-physics framework for a robot to build an expressive geometry and dynamics model of a single rigid body, using a seconds-long RGBD video and the robot’s proprioception. While the computer vision community has built powerful visual 3D perception algorithms, cluttered environments with heavy occlusions can limit the visibility of objects of interest. However, observed motion of partially occluded objects can imply physical interactions took place, such as contact with a robot or the environment. These inferred contacts can supplement the visible geometry with "physible geometry," which best explains the observed object motion through physics. Vysics uses a vision-based tracking and reconstruction method, BundleSDF, to estimate the trajectory and the visible geometry from an RGBD video, and an odometry-based model learning method, Physics Learning Library (PLL), to infer the "physible" geometry from the trajectory through implicit contact dynamics optimization. The visible and "physible" geometries jointly factor into optimizing a signed distance function (SDF) to represent the object shape. Vysics does not require pretraining, nor tactile or force sensors. Compared with vision-only methods, Vysics yields object models with higher geometric accuracy and better dynamics prediction in experiments where the object interacts with the robot and the environment under heavy occlusion.
@inproceedings{Bianchini2025, title = {Vysics: Object Reconstruction Under Occlusion by Fusing Vision and Contact-Rich Physics}, author = {Bianchini, Bibit and Zhu, Minghan and Sun, Mengti and Jiang, Bowen and Taylor, Camillo J. and Posa, Michael}, year = {2025}, month = jun, arxiv = {2504.18719}, booktitle = {Robotics: Science and Systems (RSS)}, website = {https://vysics-vision-and-physics.github.io/} }
Perceptive Mixed-Integer Footstep Control for Underactuated Bipedal Walking on Rough Terrain

Brian Acosta and Michael Posa

arXiv preprint arXiv:2501.19391, 2025

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Traversing rough terrain requires dynamic bipeds to stabilize themselves through foot placement without stepping in unsafe areas. Planning these footsteps online is challenging given non-convexity of the safe terrain, and imperfect perception and state estimation. This paper addresses these challenges with a full-stack perception and control system for achieving underactuated walking on discontinuous terrain. First, we develop model-predictive footstep control (MPFC), a single mixed-integer quadratic program which assumes a convex polygon terrain decomposition to optimize over discrete foothold choice, footstep position, ankle torque, template dynamics, and footstep timing at over 100 Hz. We then propose a novel approach for generating convex polygon terrain decompositions online. Our perception stack decouples safe-terrain classification from fitting planar polygons, generating a temporally consistent terrain segmentation in real time using a single CPU thread. We demonstrate the performance of our perception and control stack through outdoor experiments with the underactuated biped Cassie, achieving state of the art perceptive bipedal walking on discontinuous terrain.
@article{Acosta2025, title = {Perceptive Mixed-Integer Footstep Control for Underactuated Bipedal Walking on Rough Terrain}, author = {Acosta, Brian and Posa, Michael}, year = {2025}, month = jan, journal = {arXiv preprint arXiv:2501.19391}, arxiv = {2501.19391}, youtube = {JK16KJXJxi4} }
Impact-Invariant Control: Maximizing Control Authority During Impacts

William Yang and Michael Posa

arXiv preprint arXiv:2303.00817, 2024

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When legged robots impact their environment, they undergo large changes in their velocities in a short amount of time. Measuring and applying feedback to these velocities is challenging, further complicated by uncertainty in the impact model and impact timing. This work proposes a general framework for adapting feedback control during impact by projecting the control objectives to a subspace that is invariant to the impact event. The resultant controller is robust to uncertainties in the impact event while maintaining maximum control authority over the impact-invariant subspace. We demonstrate the improved performance of the projection over other commonly used heuristics on a walking controller for a planar five-link-biped. The projection is also applied to jumping, box jumping on to a platform 0.4 m tall, and running controllers for the compliant 3D bipedal robot, Cassie. The modification is easily applied to these various controllers and is a critical component to deploying on the physical robot.
@article{Yang2023, title = {Impact-Invariant Control: Maximizing Control Authority During Impacts}, author = {Yang, William and Posa, Michael}, year = {2024}, month = jul, journal = {arXiv preprint arXiv:2303.00817}, arxiv = {2303.00817}, youtube = {_v_CKU47znQ} }

Conference and Journal

Dynamic On-Palm Manipulation via Controlled Sliding

Will Yang and Michael Posa

In Robotics: Science and Systems (RSS), 2024

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Non-prehensile manipulation enables fast interactions with objects by circumventing the need to grasp and ungrasp as well as handling objects that cannot be grasped through force closure. Current approaches to non-prehensile manipulation focus on static contacts, avoiding the underactuation that comes with sliding. However, the ability to control sliding contact, essentially removing the no-slip constraint, opens up new possibilities in dynamic manipulation. In this paper, we explore a challenging dynamic non-prehensile manipulation task that requires the consideration of the full spectrum of hybrid contact modes. We leverage recent methods in contact-implicit MPC to handle the multi-modal planning aspect of the task. We demonstrate, with careful consideration of integration between the simple model used for MPC and the low-level tracking controller, how contact-implicit MPC can be adapted to dynamic tasks. Surprisingly, despite the known inaccuracies of frictional rigid contact models, our method is able to react to these inaccuracies while still quickly performing the task. Moreover, we do not use common aids such as reference trajectories or motion primitives, highlighting the generality of our approach. To the best of our knowledge, this is the first application of contact-implicit MPC to a dynamic manipulation task in three dimensions.
@inproceedings{Yang2024, title = {Dynamic On-Palm Manipulation via Controlled Sliding}, author = {Yang, Will and Posa, Michael}, year = {2024}, month = jul, arxiv = {2405.08731}, booktitle = {Robotics: Science and Systems (RSS)}, website = {https://dynamic-controlled-sliding.github.io/}, url = {https://roboticsconference.org/program/papers/12/} }
Consensus Complementarity Control for Multi-Contact MPC

Alp Aydinoglu, Adam Wei, Wei-Cheng Huang, and Michael Posa

IEEE Transactions on Robotics (TRO), 2024

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We propose a hybrid model predictive control algorithm, consensus complementarity control (C3), for systems that make and break contact with their environment. Many state-of-the-art controllers for tasks which require initiating contact with the environment, such as locomotion and manipulation, require a priori mode schedules or are too computationally complex to run at real-time rates. We present a method based on the alternating direction method of multipliers (ADMM) that is capable of high-speed reasoning over potential contact events. Via a consensus formulation, our approach enables parallelization of the contact scheduling problem. We validate our results on five numerical examples, including four high-dimensional frictional contact problems, and a physical experimentation on an underactuated multi-contact system. We further demonstrate the effectiveness of our method on a physical experiment accomplishing a high-dimensional, multi-contact manipulation task with a robot arm.
@article{Aydinoglu2024, title = {Consensus Complementarity Control for Multi-Contact MPC}, author = {Aydinoglu, Alp and Wei, Adam and Huang, Wei-Cheng and Posa, Michael}, year = {2024}, month = jul, journal = {IEEE Transactions on Robotics (TRO)}, youtube = {L57Jz3dPwO8}, arxiv = {2304.11259}, doi = {10.1109/TRO.2024.3435423}, url = {https://ieeexplore.ieee.org/document/10614849} }
Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning

Wei-Cheng Huang*, Alp Aydinoglu*, Wanxin Jin, and Michael Posa

In IEEE International Conference on Robotics and Automation (ICRA), 2024

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The hybrid nature of multi-contact robotic systems, due to making and breaking contact with the environment, creates significant challenges for high-quality control. Existing model-based methods typically rely on either good prior knowledge of the multi-contact model or require significant offline model tuning effort, thus resulting in low adaptability and robustness. In this paper, we propose a real-time adaptive multi-contact model predictive control framework, which enables online adaption of the hybrid multi-contact model and continuous improvement of the control performance for contact-rich tasks. This framework includes an adaption module, which continuously learns a residual of the hybrid model to minimize the gap between the prior model and reality, and a real-time multi-contact MPC controller. We demonstrated the effectiveness of the framework in synthetic examples, and applied it on hardware to solve contact-rich manipulation tasks, where a robot uses its end-effector to roll different unknown objects on a table to track given paths. The hardware experiments show that with a rough prior model, the multi-contact MPC controller adapts itself on-the-fly with an adaption rate around 20 Hz and successfully manipulates previously unknown objects with non-smooth surface geometries. Accompanying media can be found at: https://sites.google.com/view/adaptive-contact-implicit-mpc/home
@inproceedings{Huang2024, title = {Adaptive Contact-Implicit Model Predictive Control with Online Residual Learning}, author = {Huang, Wei-Cheng and Aydinoglu, Alp and Jin, Wanxin and Posa, Michael}, year = {2024}, month = may, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, arxiv = {2310.09893}, url = {https://ieeexplore.ieee.org/document/10610416}, doi = {10.1109/ICRA57147.2024.10610416}, youtube = {kNssB2PIgos}, website = {https://sites.google.com/view/adaptive-contact-implicit-mpc/home} }
Enhancing Task Performance of Learned Simplified Models via Reinforcement Learning

Hien Bui and Michael Posa

In IEEE International Conference on Robotics and Automation (ICRA), 2024

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In contact-rich tasks, the hybrid, multi-modal nature of contact dynamics poses great challenges in model representation, planning, and control. Recent efforts have attempted to address these challenges via data-driven methods, learning dynamical models in combination with model predictive control. Those methods, while effective, rely solely on minimizing forward prediction errors to hope for better task performance with MPC controllers. This weak correlation can result in data inefficiency as well as limitations to overall performance. In response, we propose a novel strategy: using a policy gradient algorithm to find a simplified dynamics model that explicitly maximizes task performance. Specifically, we parameterize the stochastic policy as the perturbed output of the MPC controller, thus, the learned model representation can directly associate with the policy or task performance. We apply the proposed method to contact-rich tasks where a three-fingered robotic hand manipulates previously unknown objects. Our method significantly enhances task success rate by up to 15% in manipulating diverse objects compared to the existing method while sustaining data efficiency. Our method can solve some tasks with success rates of 70% or higher using under 30 minutes of data. All videos and codes are available at https://sites.google.com/view/lcs-rl.
@inproceedings{Bui2024, title = {Enhancing Task Performance of Learned Simplified Models via Reinforcement Learning}, author = {Bui, Hien and Posa, Michael}, year = {2024}, month = may, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, arxiv = {2310.09714}, website = {https://sites.google.com/view/lcs-rl}, url = {https://ieeexplore.ieee.org/document/10611461}, doi = {10.1109/ICRA57147.2024.10611461} }
Reinforcement Learning for Reduced-order Models of Legged Robots

Yu-Ming Chen, Hien Bui, and Michael Posa

In IEEE International Conference on Robotics and Automation (ICRA), 2024

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Model-based approaches for planning and control for bipedal locomotion have a long history of success. It can provide stability and safety guarantees while being effective in accomplishing many locomotion tasks. Model-free reinforcement learning, on the other hand, has gained much popularity in recent years due to computational advancements. It can achieve high performance in specific tasks, but it lacks physical interpretability and flexibility in re-purposing the policy for a different set of tasks. For instance, we can initially train a neural network (NN) policy using velocity commands as inputs. However, to handle new task commands like desired hand or footstep locations at a desired walking velocity, we must retrain a new NN policy. In this work, we attempt to bridge the gap between these two bodies of work on a bipedal platform. We formulate a model-based reinforcement learning problem to learn a reduced-order model (ROM) within a model predictive control (MPC). Results show a 49 improvement in viable task region size and a 21% reduction in motor torque cost. All videos and code are available at https://sites.google.com/view/ymchen/research/rl-for-roms.
@inproceedings{Chen2024, title = {Reinforcement Learning for Reduced-order Models of Legged Robots}, author = {Chen, Yu-Ming and Bui, Hien and Posa, Michael}, year = {2024}, month = may, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, arxiv = {2310.09873}, url = {https://ieeexplore.ieee.org/document/10610747/}, doi = {10.1109/ICRA57147.2024.10610747}, website = {https://sites.google.com/view/ymchen/research/rl-for-roms} }
Set-Valued Rigid Body Dynamics for Simultaneous, Inelastic, Frictional Impacts

Mathew Halm and Michael Posa

The International Journal of Robotics Research, 2024

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Robotic manipulation and locomotion often entail nearly-simultaneous collisions—such as heel and toe strikes during a foot step—with outcomes that are extremely sensitive to the order in which impacts occur. Robotic simulators and state estimation commonly lack the fidelity and accuracy to predict this ordering, and instead pick one with a heuristic. This discrepancy degrades performance when model-based controllers and policies learned in simulation are placed on a real robot. We reconcile this issue with a set-valued rigid-body model which generates a broad set of outcomes to simultaneous frictional impacts with any impact ordering. We first extend Routh’s impact model to multiple impacts by reformulating it as a differential inclusion (DI), and show that any solution will resolve all impacts in finite time. By considering time as a state, we embed this model into another DI which captures the continuous-time evolution of rigid body dynamics, and guarantee existence of solutions. We finally cast simulation of simultaneous impacts as a linear complementarity problem (LCP), and develop an algorithm for tight approximation of the post-impact velocity set with probabilistic guarantees. We demonstrate our approach on several examples drawn from manipulation and legged locomotion, and compare the predictions to other models of rigid and compliant collisions.
@article{Halm2023, title = {Set-Valued Rigid Body Dynamics for Simultaneous, Inelastic, Frictional Impacts}, author = {Halm, Mathew and Posa, Michael}, journal = {The International Journal of Robotics Research}, arxiv = {2103.15714}, year = {2024}, url = {https://journals.sagepub.com/doi/10.1177/02783649241236860}, doi = {10.1177/02783649241236860} }
Beyond Inverted Pendulums: Task-optimal Simple Models of Legged Locomotion

Yu-Ming Chen, Jianshu Hu, and Michael Posa

IEEE Transactions on Robotics (TRO), 2024

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Reduced-order models (ROM) are popular in online motion planning due to their simplicity. A good ROM for control captures critical task-relevant aspects of the full dynamics while remaining low dimensional. However, planning within the reduced-order space unavoidably constrains the full model, and hence we sacrifice the full potential of the robot. In the community of legged locomotion, this has lead to a search for better model extensions, but many of these extensions require human intuition, and there has not existed a principled way of evaluating the model performance and discovering new models. In this work, we propose a model optimization algorithm that automatically synthesizes reduced-order models, optimal with respect to a user-specified distribution of tasks and corresponding cost functions. To demonstrate our work, we optimized models for a bipedal robot Cassie. We show in simulation that the optimal ROM reduces the cost of Cassie’s joint torques by up to 23% and increases its walking speed by up to 54%. We also show hardware result that the real robot walks on flat ground with 10% lower torque cost. All videos and code can be found at https://sites.google.com/view/ymchen/research/optimal-rom.
@article{Chen2023b, title = {Beyond Inverted Pendulums: Task-optimal Simple Models of Legged Locomotion}, author = {Chen, Yu-Ming and Hu, Jianshu and Posa, Michael}, year = {2024}, journal = {IEEE Transactions on Robotics (TRO)}, arxiv = {2301.02075}, youtube = {NXtue18TsvE}, doi = {10.1109/TRO.2024.3386390}, volume = {40}, number = {}, pages = {2582-2601}, url = {https://ieeexplore.ieee.org/document/10494916}, website = {https://sites.google.com/view/ymchen/research/optimal-rom} }
Task-Driven Hybrid Model Reduction for Dexterous Manipulation

Wanxin Jin and Michael Posa

IEEE Transactions on Robotics (TRO), 2024

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In contact-rich tasks, like dexterous manipulation, the hybrid nature of making and breaking contact creates challenges for model representation and control. For example, choosing and sequencing contact locations for in-hand manipulation, where there are thousa nds of potential hybrid modes, is not generally tractable. In this paper, we are inspired by the observation that far fewer modes are actually necessary to accomplish many tasks. Building on our prior work learning hybrid models, represented as linear complementarity systems, we find a reduced-order hybrid model requiring only a limited number of task-relevant modes. This simplified representation, in combination with model predictive control, enables real-time control yet is sufficient for achieving high performance. We demonstrate the proposed method first on synthetic hybrid systems, reducing the mode count by multiple orders of magnitude while achieving task performance loss of less than 5%. We also apply the proposed method to a three-fingered robotic hand manipulating a previously unknown object. With no prior knowledge, we achieve state-of-the-art closed-loop performance in less than five minutes of online learning.
@article{Jin2024, title = {Task-Driven Hybrid Model Reduction for Dexterous Manipulation}, author = {Jin, Wanxin and Posa, Michael}, journal = {IEEE Transactions on Robotics (TRO)}, url = {https://ieeexplore.ieee.org/document/10415517}, year = {2024}, arxiv = {2211.16657}, doi = {10.1109/TRO.2024.3359531}, volume = {40}, number = {}, pages = {1774-1794}, youtube = {OvhTOQoagTM}, website = {https://wanxinjin.github.io/td_hybridreduction/} }
Bipedal Walking on Constrained Footholds with MPC Footstep Control

Brian Acosta and Michael Posa

In IEEE-RAS International Conference on Humanoid Robotics, 2023

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Bipedal robots promise the ability to traverse rough terrain quickly and efficiently, and indeed, humanoid robots can now use strong ankles and careful foot placement to traverse discontinuous terrain. However, more agile underactuated bipeds have small feet and weak ankles, and must constantly adjust their planned footstep position to maintain balance. We introduce a new model-predictive footstep controller which jointly optimizes over the robot’s discrete choice of stepping surface, impending footstep position sequence, ankle torque in the sagittal plane, and center of mass trajectory, to track a velocity command. The controller is formulated as a single Mixed Integer Quadratic Program (MIQP) which is solved at 50-200 Hz, depending on terrain complexity. We implement a state of the art real-time elevation mapping and convex terrain decomposition framework to inform the controller of its surroundings in the form on convex polygons representing steppable terrain. We investigate the capabilities and challenges of our approach through hardware experiments on the underactuated biped Cassie.
@inproceedings{Acosta2023, title = {Bipedal Walking on Constrained Footholds with MPC Footstep Control}, author = {Acosta, Brian and Posa, Michael}, year = {2023}, month = dec, booktitle = {IEEE-RAS International Conference on Humanoid Robotics}, youtube = {aTI6s2a3JSg}, arxiv = {2309.07993}, url = {https://ieeexplore.ieee.org/abstract/document/10375170}, doi = {10.1109/Humanoids57100.2023.10375170} }
Im2Contact: Vision-Based Contact Localization Without Touch or Force Sensing

Leon Kim, Yunshuang Li, Michael Posa, and Dinesh Jayaraman

In Conference on Robot Learning (CoRL), 2023

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Contacts play a critical role in most manipulation tasks. Robots today mainly use proximal touch/force sensors to sense contacts, but the information they provide must be calibrated and is inherently local, with practical applications relying either on extensive surface coverage or restrictive assumptions to resolve ambiguities. We propose a vision-based extrinsic contact localization task: with only a single RGB-D camera view of a robot workspace, identify when and where an object held by the robot contacts the rest of the environment. We show that careful task-attuned design is critical for a neural network trained in simulation to discover solutions that transfer well to a real robot. Our final approach \methodname demonstrates the promise of versatile general-purpose contact perception from vision alone, performing well for localizing various contact types (point, line, or planar; sticking, sliding, or rolling; single or multiple), and even under occlusions in its camera view.
@inproceedings{Kim2023, title = {Im2Contact: Vision-Based Contact Localization Without Touch or Force Sensing}, author = {Kim, Leon and Li, Yunshuang and Posa, Michael and Jayaraman, Dinesh}, year = {2023}, month = nov, booktitle = {Conference on Robot Learning (CoRL)}, url = {https://openreview.net/forum?id=h8halpbqB-}, website = {https://sites.google.com/view/im2contact/home} }
Simultaneous Learning of Contact and Continuous Dynamics

Bibit Bianchini, Mathew Halm, and Michael Posa

In Conference on Robot Learning (CoRL), 2023

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Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joint friction lack clear first-principles models and are usually ignored by physics simulators. Further, numerically-stiff contact dynamics can make common model-building approaches struggle. We propose a method to simultaneously learn contact and continuous dynamics of a novel, possibly multi-link object by observing its motion through contact-rich trajectories. We formulate a system identification process with a loss that infers unmeasured contact forces, penalizing their violation of physical constraints and laws of motion given current model parameters. Our loss is unlike prediction-based losses used in differentiable simulation. Using a new dataset of real articulated object trajectories and an existing cube toss dataset, our method outperforms differentiable simulation and end-to-end alternatives with more data efficiency.
@inproceedings{Bianchini2023, title = {Simultaneous Learning of Contact and Continuous Dynamics}, author = {Bianchini, Bibit and Halm, Mathew and Posa, Michael}, year = {2023}, month = nov, booktitle = {Conference on Robot Learning (CoRL)}, url = {https://proceedings.mlr.press/v229/bianchini23a.html}, website = {https://sites.google.com/view/continuous-contact-nets/home}, code = {https://github.com/ebianchi/dair_pll}, youtube = {uMCLCIzbgJo} }
Optimization-Based Control for Dynamic Legged Robots

Patrick M Wensing, Michael Posa, Yue Hu, Adrien Escande, Nicolas Mansard, and Andrea Del Prete

IEEE Transactions on Robotics (TRO), 2023

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In a world designed for legs, quadrupeds, bipeds, and humanoids have the opportunity to impact emerging robotics applications from logistics, to agriculture, to home assistance. The goal of this survey is to cover the recent progress toward these applications that has been driven by model-based optimization for the real-time generation and control of movement. The majority of the research community has converged on the idea of generating locomotion control laws by solving an optimal control problem (OCP) in either a model-based or data-driven manner. However, solving the most general of these problems online remains intractable due to complexities from intermittent unidirectional contacts with the environment, and from the many degrees of freedom of legged robots. This survey covers methods that have been pursued to make these OCPs computationally tractable, with specific focus on how environmental contacts are treated, how the model can be simplified, and how these choices affect the numerical solution methods employed. The survey focuses on model-based optimization while paving its way for broader combination with learning-based formulations to accelerate progress in this growing field.
@article{Wensing2023, title = {Optimization-Based Control for Dynamic Legged Robots}, author = {Wensing, Patrick M and Posa, Michael and Hu, Yue and Escande, Adrien and Mansard, Nicolas and Del Prete, Andrea}, year = {2023}, month = oct, journal = {IEEE Transactions on Robotics (TRO)}, url = {https://ieeexplore.ieee.org/document/10286076}, doi = {10.1109/TRO.2023.3324580}, arxiv = {2211.11644} }
Integrable Whole-body Orientation Coordinates for Legged Robots

Yu-Ming Chen, Gabriel Nelson, Robert Griffin, Michael Posa, and Jerry Pratt

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023

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Complex multibody legged robots can have complex rotational control challenges. In this paper, we propose a concise way to understand and formulate a whole-body orientation that (i) depends on system configuration only and not a history of motion, (ii) can be representative of the orientation of the entire system while not be attached to any specific link, and (iii) has a rate of change that approximates total system angular momentum. We relate this orientation coordinate to past work, and discuss and demonstrate, including on hardware, several different uses for it.
@inproceedings{Chen2023, title = {Integrable Whole-body Orientation Coordinates for Legged Robots}, author = {Chen, Yu-Ming and Nelson, Gabriel and Griffin, Robert and Posa, Michael and Pratt, Jerry}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023}, month = oct, arxiv = {2210.08111}, publisher = {https://ieeexplore.ieee.org/document/10341531}, doi = {10.1109/IROS55552.2023.10341531}, youtube = {AZqAWImoAe4} }
Real-Time Multi-Contact Model Predictive Control via ADMM

Alp Aydinoglu and Michael Posa

In IEEE International Conference on Robotics and Automation (ICRA), 2022

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We propose a general hybrid model predictive control algorithm, consensus complementarity control (C3), for systems that make and break contact with their environment. Many state-of-the-art controllers for tasks which require initiating contact with the environment, such as locomotion and manipulation, require a priori mode schedules or are so computationally complex that they cannot run at real-time rates. We present a method, based on the alternating direction method of multipliers (ADMM), capable of highspeed reasoning over potential contact events. Via a consensus formulation, our approach enables parallelization of the contact scheduling problem. We validate our results on three numerical examples, including two frictional contact problems, and physical experimentation on an underactuated multi-contact system.
@inproceedings{Aydinoglu2022, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, author = {Aydinoglu, Alp and Posa, Michael}, arxiv = {2109.07076}, month = sep, title = {{Real-Time Multi-Contact Model Predictive Control via ADMM}}, year = {2022}, code = {https://github.com/AlpAydinoglu/coptimal}, youtube = {HyEv-pK9Qfs}, url = {https://ieeexplore.ieee.org/document/9811957}, doi = {10.1109/ICRA46639.2022.9811957} }
Generalization Bounded Implicit Learning of Nearly Discontinuous Functions

Bibit Bianchini, Mathew Halm, Nikolai Matni, and Michael Posa

In Proceedings of The 4th Annual Learning for Dynamics and Control Conference (L4DC), 2022

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Inspired by recent strides in empirical efficacy of implicit learning in many robotics tasks, we seek to understand the theoretical benefits of implicit formulations in the face of nearly discontinuous functions, common characteristics for systems that make and break contact with the environment such as in legged locomotion and manipulation. We present and motivate three formulations for learning a function: one explicit and two implicit. We derive generalization bounds for each of these three approaches, exposing where explicit and implicit methods alike based on prediction error losses typically fail to produce tight bounds, in contrast to other implicit methods with violation-based loss definitions that can be fundamentally more robust to steep slopes. Furthermore, we demonstrate that this violation implicit loss can tightly bound graph distance, a quantity that often has physical roots and handles noise in inputs and outputs alike, instead of prediction losses which consider output noise only. Our insights into the generalizability and physical relevance of violation implicit formulations match evidence from prior works and are validated through a toy problem, inspired by rigid-contact models and referenced throughout our theoretical analysis.
@inproceedings{Bianchini2022, title = {Generalization Bounded Implicit Learning of Nearly Discontinuous Functions}, author = {Bianchini, Bibit and Halm, Mathew and Matni, Nikolai and Posa, Michael}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference (L4DC)}, pages = {1112--1124}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, url = {https://proceedings.mlr.press/v168/bianchini22a.html}, arxiv = {2112.06881} }
Learning Linear Complementarity Systems

Wanxin Jin, Alp Aydinoglu, Mathew Halm, and Michael Posa

In Proceedings of The 4th Annual Learning for Dynamics and Control Conference (L4DC), 2022

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This paper investigates the learning, or system identification, of a class of piecewise-affine dynamical systems known as linear complementarity systems (LCSs). We propose a violation-based loss which enables efficient learning of the LCS parameterization, without prior knowledge of the hybrid mode boundaries, using gradient-based methods. The proposed violation-based loss incorporates both dynamics prediction loss and a novel complementarity - violation loss. We show several properties attained by this loss formulation, including its differentiability, the efficient computation of first- and second-order derivatives, and its relationship to the traditional prediction loss, which strictly enforces complementarity. We apply this violation-based loss formulation to learn LCSs with tens of thousands of (potentially stiff) hybrid modes. The results demonstrate a state-of-the-art ability to identify piecewise-affine dynamics, outperforming methods which must differentiate through non-smooth linear complementarity problems.
@inproceedings{Jin2022, title = {Learning Linear Complementarity Systems}, author = {Jin, Wanxin and Aydinoglu, Alp and Halm, Mathew and Posa, Michael}, booktitle = {Proceedings of The 4th Annual Learning for Dynamics and Control Conference (L4DC)}, pages = {1137--1149}, year = {2022}, editor = {Firoozi, Roya and Mehr, Negar and Yel, Esen and Antonova, Rika and Bohg, Jeannette and Schwager, Mac and Kochenderfer, Mykel}, volume = {168}, series = {Proceedings of Machine Learning Research}, month = {23--24 Jun}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v168/jin22a/jin22a.pdf}, url = {https://proceedings.mlr.press/v168/jin22a.html}, arxiv = {2112.13284}, code = {https://github.com/DAIRLab/Learning-LCS} }
Stabilization of Complementarity Systems via Contact-Aware Controllers

Alp Aydinoglu, Philip Sieg, Victor Preciado, and Michael Posa

IEEE Transactions on Robotics (TRO), 2022

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We propose a framework for provably stable local control of multi-contact robotic systems, directly utilizing force measurements and exploiting the complementarity structure of contact dynamics. Since many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatoric structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. In this work, we present a control framework which can close the loop on tactile sensors. Critically, this framework is non-combinatoric, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on multiple examples, including underactuated multi-contact problems, quasi-static friction problems and a high-dimensional problem with ten contacts.
@article{Aydinoglu2021b, title = {Stabilization of Complementarity Systems via Contact-Aware Controllers}, author = {Aydinoglu, Alp and Sieg, Philip and Preciado, Victor and Posa, Michael}, journal = {IEEE Transactions on Robotics (TRO)}, year = {2022}, youtube = {fZiJh7coMXc}, arxiv = {2008.02104}, doi = {10.1109/TRO.2021.3120931}, url = {https://ieeexplore.ieee.org/document/9614168}, volume = {38}, number = {3}, pages = {1735-1754} }
Validating Robotics Simulators on Real World Impacts

Brian Acosta*, William Yang*, and Michael Posa

IEEE Robotics and Automation Letters (RA-L), 2022

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A realistic simulation environment is an essential tool in every roboticist’s toolkit, with uses ranging from planning and control to training policies with reinforcement learning. Despite the centrality of simulation in modern robotics, little work has been done to compare the performance of robotics simulators against real-world data, especially for scenarios involving dynamic motions with high speed impact events. Handling dynamic contact is the computational bottleneck for most simulations, and thus the modeling and algorithmic choices surrounding impacts and friction form the largest distinctions between popular tools. Here, we evaluate the ability of several simulators to reproduce real-world trajectories involving impacts. Using experimental data, we identify system-specific contact parameters of popular simulators Drake, MuJoCo, and Bullet, analyzing the effects of modeling choices around these parameters. For the simple example of a cube tossed onto a table, simulators capture inelastic impacts well while failing to capture elastic impacts. For the higher-dimensional case of a Cassie biped landing from a jump, the simulators capture the bulk motion well but the accuracy is limited by numerous model differences between the real robot and the simulators.
@article{Acosta2022, title = {Validating Robotics Simulators on Real World Impacts}, author = {Acosta, Brian and Yang, William and Posa, Michael}, journal = {IEEE Robotics and Automation Letters (RA-L)}, arxiv = {2110.00541}, year = {2022}, youtube = {Ao6xJt4TpgU}, url = {https://ieeexplore.ieee.org/document/9772943}, volume = {7}, number = {3}, pages = {6471-6478}, doi = {10.1109/LRA.2022.3174367} }
Fundamental Challenges in Deep Learning for Stiff Contact Dynamics

Mihir Parmar*, Mathew Halm*, and Michael Posa

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

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Frictional contact has been extensively studied as the core underlying behavior of legged locomotion and manipulation, and its nearly-discontinuous nature makes planning and control difficult even when an accurate model of the robot is available. Here, we present empirical evidence that learning an accurate model in the first place can be confounded by contact, as modern deep learning approaches are not designed to capture this non-smoothness. We isolate the effects of contact’s non-smoothness by varying the mechanical stiffness of a compliant contact simulator. Even for a simple system, we find that stiffness alone dramatically degrades training processes, generalization, and data-efficiency. Our results raise serious questions about simulated testing environments which do not accurately reflect the stiffness of rigid robotic hardware. Significant additional investigation will be necessary to fully understand and mitigate these effects, and we suggest several avenues for future study.
@inproceedings{Parmar2021, arxiv = {2103.15406}, author = {Parmar, Mihir and Halm, Mathew and Posa, Michael}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, month = mar, title = {{Fundamental Challenges in Deep Learning for Stiff Contact Dynamics}}, year = {2021}, youtube = {G0V3_oQCGTk}, code = {https://github.com/DAIRLab/ContactLearningBias}, website = {https://sites.google.com/view/contact-learning-bias}, url = {https://ieeexplore.ieee.org/document/9636383} }
Impact Invariant Control with Applications to Bipedal Locomotion

William Yang and Michael Posa

In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021

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When legged robots impact their environment, they undergo large changes in their velocities in a small amount of time. Measuring and applying feedback to these velocities is challenging, and is further complicated due to uncertainty in the impact model and impact timing. This work proposes a general framework for adapting feedback control during impact by projecting the control objectives to a subspace that is invariant to the impact event. The resultant controller is robust to uncertainties in the impact event while maintaining maximum control authority over the impact invariant subspace. We demonstrate the utility of the projection on a walking controller for a planar five-link-biped and on a jumping controller for a compliant 3D bipedal robot, Cassie. The effectiveness of our method is shown to translate well on hardware.
@inproceedings{Yang2021, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, author = {Yang, William and Posa, Michael}, arxiv = {2103.06907}, file = {::}, month = mar, title = {{Impact Invariant Control with Applications to Bipedal Locomotion}}, year = {2021}, youtube = {EF_tWrT-xeQ}, url = {https://ieeexplore.ieee.org/document/9636094} }
Stability analysis of complementarity systems with neural network controllers

Alp Aydinoglu, Fazlyab Mahyar, Manfred Morari, and Michael Posa

In Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control (HSCC), 2021

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Complementarity problems, a class of mathematical optimization problems with orthogonality constraints, are widely used in many robotics tasks, such as locomotion and manipulation, due to their ability to model non-smooth phenomena (e.g., contact dynamics). In this paper, we propose a method to analyze the stability of complementarity systems with neural network controllers. First, we introduce a method to represent neural networks with rectified linear unit (ReLU) activations as the solution to a linear complementarity problem. Then, we show that systems with ReLU network controllers have an equivalent linear complementarity system (LCS) description. Using the LCS representation, we turn the stability verification problem into a linear matrix inequality (LMI) feasibility problem. We demonstrate the approach on several examples, including multi-contact problems and friction models with non-unique solutions.
@inproceedings{Aydinoglu2021, author = {Aydinoglu, Alp and Mahyar, Fazlyab and Morari, Manfred and Posa, Michael}, booktitle = {Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control (HSCC)}, title = {Stability analysis of complementarity systems with neural network controllers}, arxiv = {2011.07626}, year = {2021}, url = {https://dl.acm.org/doi/abs/10.1145/3447928.3456651} }
Contact-Aware Controller Design for Complementarity Systems

Alp Aydinoglu, V.M. Victor Preciado, and Michael Posa

In IEEE International Conference on Robotics and Automation (ICRA), 2020

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While many robotic tasks, like manipulation and locomotion, are fundamentally based in making and breaking contact with the environment, state-of-the-art control policies struggle to deal with the hybrid nature of multi-contact motion. Such controllers often rely heavily upon heuristics or, due to the combinatoric structure in the dynamics, are unsuitable for real-time control. Principled deployment of tactile sensors offers a promising mechanism for stable and robust control, but modern approaches often use this data in an ad hoc manner, for instance to guide guarded moves. In this work, by exploiting the complementarity structure of contact dynamics, we propose a control framework which can close the loop on rich, tactile sensors. Critically, this framework is non-combinatoric, enabling optimization algorithms to automatically synthesize provably stable control policies. We demonstrate this approach on three different underactuated, multi-contact robotics problems.
@inproceedings{Aydinoglu2020, address = {Paris, France}, author = {Aydinoglu, Alp and Preciado, V.M. Victor and Posa, Michael}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, doi = {10.1109/ICRA40945.2020.9197568}, isbn = {9781728173955}, issn = {10504729}, title = {{Contact-Aware Controller Design for Complementarity Systems}}, year = {2020}, arxiv = {1909.11221}, url = {https://ieeexplore.ieee.org/abstract/document/9197568/} }
ContactNets: Learning Discontinuous Contact Dynamics with Smooth, Implicit Representations

Samuel Pfrommer*, Mathew Halm*, and Michael Posa

In The Conference on Robot Learning (CoRL), 2020

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Common methods for learning robot dynamics assume motion is continuous, causing unrealistic model predictions for systems undergoing discontinuous impact and stiction behavior. In this work, we resolve this conflict with a smooth, implicit encoding of the structure inherent to contact-induced discontinuities. Our method, ContactNets, learns parameterizations of inter-body signed distance and contact-frame Jacobians, a representation that is compatible with many simulation, control, and planning environments for robotics. We furthermore circumvent the need to differentiate through stiff or non-smooth dynamics with a novel loss function inspired by the principles of complementarity and maximum dissipation. Our method can predict realistic impact, non-penetration, and stiction when trained on 60 seconds of real-world data.
@inproceedings{Pfrommer2020, arxiv = {2009.11193}, author = {Pfrommer, Samuel and Halm, Mathew and Posa, Michael}, booktitle = {The Conference on Robot Learning (CoRL)}, title = {{ContactNets: Learning Discontinuous Contact Dynamics with Smooth, Implicit Representations}}, year = {2020}, code = {https://github.com/DAIRLab/contact-nets}, youtube = {I6p8JrIp1Es}, url = {https://proceedings.mlr.press/v155/pfrommer21a.html} }
Optimal Reduced-order Modeling of Bipedal Locomotion

Yu-Ming Chen and Michael Posa

In IEEE International Conference on Robotics and Automation (ICRA), 2020

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State-of-the-art approaches to legged locomotion are widely dependent on the use of models like the linear inverted pendulum (LIP) and the spring-loaded inverted pendulum (SLIP), popular because their simplicity enables a wide array of tools for planning, control, and analysis. However, they inevitably limit the ability to execute complex tasks or agile maneuvers. In this work, we aim to automatically synthesize models that remain low-dimensional but retain the capabilities of the high-dimensional system. For example, if one were to restore a small degree of complexity to LIP, SLIP, or a similar model, our approach discovers the form of that additional complexity which optimizes performance. In this paper, we define a class of reduced-order models and provide an algorithm for optimization within this class. To demonstrate our method, we optimize models for walking at a range of speeds and ground inclines, for both a five-link model and the Cassie bipedal robot.
@inproceedings{Chen2020, address = {Paris, France}, author = {Chen, Yu-Ming and Posa, Michael}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, doi = {10.1109/ICRA40945.2020.9197004}, isbn = {9781728173955}, issn = {10504729}, title = {{Optimal Reduced-order Modeling of Bipedal Locomotion}}, year = {2020}, arxiv = {1909.10111}, youtube = {RQfBb8jRDGk}, url = {https://ieeexplore.ieee.org/abstract/document/9197004/} }
Modeling and Analysis of Non-unique Behaviors in Multiple Frictional Impacts

Mathew Halm and Michael Posa

In Robotics: Science and Systems (RSS), 2019

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Many fundamental challenges in robotics, based in manipulation or locomotion, require making and breaking contact with the environment. Models that address frictional contact must be inherently non-smooth; rigid-body models are especially popular, as they often lead to mathematically and computationally tractable approaches. However, when two or more impacts occur simultaneously, the precise sequencing of impact forces is generally unknown, leading to the potential for multiple possible outcomes. This simultaneity is far from pathological, and occurs in many common robotics applications. In this work, we present an approach to capturing simultaneous frictional impacts, represented as a differential inclusion. Solutions to our model, an extension to multiple contacts of Routh’s graphical method, naturally capture the set of potential post-impact velocities. We prove that, under modest conditions, the presented approach is guaranteed to terminate. This is, to the best of our knowledge, the first such guarantee for simultaneous frictional impacts.
@inproceedings{Halm2019, address = {Freiburg im Breisgau, Germany}, arxiv = {1902.01462}, author = {Halm, Mathew and Posa, Michael}, booktitle = {Robotics: Science and Systems (RSS)}, title = {{Modeling and Analysis of Non-unique Behaviors in Multiple Frictional Impacts}}, year = {2019}, url = {http://roboticsproceedings.org/rss15/p22.pdf} }
A Quasi-static Model and Simulation Approach for Pushing, Grasping, and Jamming

Mathew Halm and Michael Posa

In The Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018

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Quasi-static models of robotic motion with frictional contact provide a computationally efficient framework for analysis and have been widely used for planning and control of non-prehensile manipulation. In this work, we present a novel quasi-static model of planar manipulation that directly maps commanded manipulator velocities to object motion. While quasi-static models have traditionally been unable to capture grasping and jamming behaviors, our approach solves this issue by explicitly modeling the limiting behavior of a velocity-controlled manipulator. We retain the precise modeling of surface contact pressure distributions and efficient computation of contact-rich behaviors of previous methods and additionally prove existence of solutions for any desired manipulator motion. We derive continuous and time-stepping formulations, both posed as tractable Linear Complementarity Problems (LCPs).
@inproceedings{Halm2018, address = {Merida, Mexico}, author = {Halm, Mathew and Posa, Michael}, booktitle = {The Workshop on the Algorithmic Foundations of Robotics (WAFR)}, keywords = {dynamics,linear complementarity problems,manipulation and grasping,quasi-static motion,rigid body motion,simulation}, title = {{A Quasi-static Model and Simulation Approach for Pushing, Grasping, and Jamming}}, year = {2018}, arxiv = {1902.03487}, url = {https://link.springer.com/chapter/10.1007/978-3-030-44051-0_29} }
Balancing and Step Recovery Capturability via Sums-of-Squares Optimization

Michael Posa, Twan Koolen, and Russ Tedrake

In Robotics: Science and Systems (RSS), 2017

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A fundamental requirement for legged robots is to maintain balance and prevent potentially damaging falls whenever possible. As a response to outside disturbances, fall prevention can be achieved by a combination of active balancing actions, e.g. through ankle torques and upper-body motion, and through reactive step placement. While it is widely accepted that stepping is required to respond to large disturbances, the limits of active motions on balancing and step recovery are only well understood for the simplest of walking models. Recent advances in convex optimization-based verification and control techniques enable a more complete understanding of the limits and capabilities of more complex models. In this work, we present an algorithmic approach for formal analysis of the viable-capture basins of walking robots, calculating both inner and outer approximations and corresponding push recovery control strategies. Extending beyond the classic Linear Inverted Pendulum Model (LIPM), we analyze a series of centroidal momentum based planar walking models, examining the effects of center of mass height, angular momentum, and impact dynamics during stepping on capturability. This formal analysis enables an explicit calculation of the differences between these models, and assessment of whether the simplest models ultimately sacrifice capability, and thus stability, when designing push recovery control policies.
@inproceedings{Posa2017, author = {Posa, Michael and Koolen, Twan and Tedrake, Russ}, booktitle = {Robotics: Science and Systems (RSS)}, doi = {10.15607/rss.2017.xiii.032}, isbn = {9780992374730}, issn = {2330765X}, title = {{Balancing and Step Recovery Capturability via Sums-of-Squares Optimization}}, volume = {13}, year = {2017}, url = {http://www.roboticsproceedings.org/rss13/p32.pdf} }
Stability analysis and control of rigid-body systems with impacts and friction

Michael Posa, Mark Tobenkin, and Russ Tedrake

IEEE Transactions on Automatic Control (TAC), 2016

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Many critical tasks in robotics, such as locomotion or manipulation, involve collisions between a rigid body and the environment or between multiple bodies. Methods based on sums-of-squares (SOS) for numerical computation of Lyapunov certificates are a powerful tool for analyzing the stability of continuous nonlinear systems, and can additionally be used to automatically synthesize stabilizing feedback controllers. Here, we present a method for applying sums-of-squares verification to rigid bodies with Coulomb friction undergoing discontinuous, inelastic impact events. The proposed algorithm explicitly generates Lyapunov certificates for stability, positive invariance, and safety over admissible (non-penetrating) states and contact forces. We leverage the complementarity formulation of contact, which naturally generates the semialgebraic constraints that define this admissible region. The approach is demonstrated on multiple robotics examples, including simple models of a walking robot, a perching aircraft, and control design of a balancing robot.
@article{Posa2016, author = {Posa, Michael and Tobenkin, Mark and Tedrake, Russ}, doi = {10.1109/TAC.2015.2459151}, issn = {00189286}, journal = {IEEE Transactions on Automatic Control (TAC)}, keywords = {Control design,Lyapunov analysis and stability verification,rigid-body dynamics with impacts and friction,sumsof-squares (sos)}, month = jun, number = {6}, pages = {1423--1437}, title = {{Stability analysis and control of rigid-body systems with impacts and friction}}, volume = {61}, year = {2016}, url = {https://ieeexplore.ieee.org/document/7163521} }
Optimization and stabilization of trajectories for constrained dynamical systems

Michael Posa, Scott Kuindersma, and Russ Tedrake

In IEEE International Conference on Robotics and Automation (ICRA), 2016

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Contact constraints, such as those between a foot and the ground or a hand and an object, are inherent in many robotic tasks. These constraints define a manifold of feasible states; while well understood mathematically, they pose numerical challenges to many algorithms for planning and controlling whole-body dynamic motions. In this paper, we present an approach to the synthesis and stabilization of complex trajectories for both fully-actuated and underactuated robots subject to contact constraints. We introduce an extension to the direct collocation trajectory optimization algorithm that naturally incorporates the manifold constraints to produce a nominal trajectory with third-order integration accuracy\97 a critical feature for achieving reliable tracking control. We adapt the classical time-varying linear quadratic regulator to produce a local cost-to-go in the tangent plane of the manifold. Finally, we descend the cost-to-go using a quadratic program that incorporates unilateral friction and torque constraints. This approach is demonstrated on three complex walking and climbing locomotion examples in simulation.
@inproceedings{Posa2016a, address = {Stockholm, Sweden}, author = {Posa, Michael and Kuindersma, Scott and Tedrake, Russ}, booktitle = {IEEE International Conference on Robotics and Automation (ICRA)}, doi = {10.1109/ICRA.2016.7487270}, isbn = {9781467380263}, issn = {10504729}, month = may, pages = {1366--1373}, title = {{Optimization and stabilization of trajectories for constrained dynamical systems}}, volume = {2016-June}, year = {2016}, youtube = {iTDtMTJ1Z14}, url = {https://ieeexplore.ieee.org/abstract/document/7487270/} }
Balance control using center of mass height variation: limitations imposed by unilateral contact

Twan Koolen, Michael Posa, and Russ Tedrake

In IEEE-RAS International Conference on Humanoid Robots, 2016

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Maintaining balance is fundamental to legged robots. The most commonly used mechanisms for balance control are taking a step, regulating the center of pressure (ankle strategies), and to a lesser extent, changing centroidal angular momentum (e.g., hip strategies). In this paper, we disregard these three mechanisms, instead focusing on a fourth: varying center of mass height. We study a 2D variable-height center of mass model, and analyze how center of mass height variation can be used to achieve balance, in the sense of convergence to a fixed point of the dynamics. In this analysis, we pay special attention to the constraint of unilateral contact forces. We first derive a necessary condition that must be satisfied to be able to achieve balance. We then present two control laws, and derive their regions of attraction in closed form. We show that one of the control laws achieves balance from any state satisfying the necessary condition for balance. Finally, we briefly discuss the relative importance of CoM height variation and other balance mechanisms.
@inproceedings{Koolen2016, author = {Koolen, Twan and Posa, Michael and Tedrake, Russ}, booktitle = {IEEE-RAS International Conference on Humanoid Robots}, doi = {10.1109/HUMANOIDS.2016.7803247}, isbn = {9781509047185}, issn = {21640580}, pages = {8--15}, publisher = {IEEE}, title = {{Balance control using center of mass height variation: limitations imposed by unilateral contact}}, year = {2016}, url = {https://ieeexplore.ieee.org/document/7803247} }
An Architecture for Online Affordance-based Perception and Whole-body Planning

Maurice Fallon, Scott Kuindersma, Sisir Karumanchi, Matthew Antone, Toby Schneider, Hongkai Dai, Claudia Pérez D’Arpino, Robin Deits, Matt DiCicco, Dehann Fourie, Twan Koolen, Pat Marion, Michael Posa, Andrés Valenzuela, Kuan-Ting Yu, Julie Shah, Karl Iagnemma, Russ Tedrake, and Seth Teller

Journal of Field Robotics (JFR), 2015

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@article{Fallon2015, author = {Fallon, Maurice and Kuindersma, Scott and Karumanchi, Sisir and Antone, Matthew and Schneider, Toby and Dai, Hongkai and D'Arpino, Claudia P{\'{e}}rez and Deits, Robin and DiCicco, Matt and Fourie, Dehann and Koolen, Twan and Marion, Pat and Posa, Michael and Valenzuela, Andr{\'{e}}s and Yu, Kuan-Ting and Shah, Julie and Iagnemma, Karl and Tedrake, Russ and Teller, Seth}, doi = {10.1002/rob.21546}, journal = {Journal of Field Robotics (JFR)}, month = mar, number = {2}, pages = {229--254}, title = {{An Architecture for Online Affordance-based Perception and Whole-body Planning}}, url = {http://doi.wiley.com/10.1002/rob.21546}, volume = {32}, year = {2015} }
A Direct Method for Trajectory Optimization of Rigid Bodies Through Contact

Michael Posa, Cecilia Cantu, and Russ Tedrake

International Journal of Robotics Research (IJRR), 2014

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Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Many critical tasks, such as locomotion and manipulation, often involve impacting the ground or objects in the environ- ment. Most state-of-the-art techniques treat the discontinuous dynamics that result from impacts as discrete modes and restrict the search for a complete path to a specified sequence through these modes. Here we present a novel method for trajectory planning of rigid body systems that contact their environment through inelastic impacts and Coulomb friction. This method eliminates the requirement for a priori mode ordering. Motivated by the formulation of multi-contact dy- namics as a Linear Complementarity Problem (LCP) for forward simulation, the proposed algorithm poses the optimization problem as a Mathematical Program with Complementarity Constraints (MPCC). We leverage Sequential Quadratic Programming (SQP) to naturally resolve contact constraint forces while simul- taneously optimizing a trajectory that satisfies the complementarity constraints. The method scales well to high dimensional systems with large numbers of possi- ble modes. We demonstrate the approach on four increasingly complex systems: rotating a pinned object with a finger, simple grasping and manipulation, planar walking with the Spring Flamingo robot, and high speed bipedal running on the FastRunner platform.
@article{Posa2014, author = {Posa, Michael and Cantu, Cecilia and Tedrake, Russ}, doi = {10.1177/0278364913506757}, journal = {International Journal of Robotics Research (IJRR)}, keywords = {Trajectory optimization,locomotion planning,optimal control,planning with contacts,planning with impacts and friction}, month = jan, number = {1}, pages = {69--81}, title = {{A Direct Method for Trajectory Optimization of Rigid Bodies Through Contact}}, url = {http://ijr.sagepub.com/content/33/1/69.short}, volume = {33}, year = {2014} }
Lyapunov Analysis of Rigid Body Systems with Impacts and Friction via Sums-of-Squares

Michael Posa, Mark Tobenkin, and Russ Tedrake

In Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control (HSCC), 2013

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Many critical tasks in robotics, such as locomotion or manipulation, involve collisions between a rigid body and the environment or between multiple bodies. Sums-of-squares (SOS) based methods for numerical computation of Lyapunov certificates are a powerful tool for analyzing the stability of continuous nonlinear systems, which can play a powerful role in motion planning and control design. Here, we present a method for applying sums-of-squares verification to rigid bodies with Coulomb friction undergoing discontinuous, inelastic impact events. The proposed algorithm explicitly generates Lyapunov certificates for stability, positive invariance, and reachability over admissible (non-penetrating) states and contact forces. We leverage the complementarity formulation of contact, which naturally generates the semialgebraic constraints that define this admissible region. The approach is demonstrated on multiple robotics examples, including simple models of a walking robot and a perching aircraft.
@inproceedings{Posa2013, author = {Posa, Michael and Tobenkin, Mark and Tedrake, Russ}, booktitle = {Proceedings of the 16th International Conference on Hybrid Systems: Computation and Control (HSCC)}, doi = {10.1145/2461328.2461340}, isbn = {9781450315678}, keywords = {Lyapunov analysis and stability verification,Rigid body dynamics with impacts and friction,Sums-of-squares}, month = apr, pages = {63--72}, publisher = {ACM}, title = {{Lyapunov Analysis of Rigid Body Systems with Impacts and Friction via Sums-of-Squares}}, volume = {1}, year = {2013}, url = {https://dl.acm.org/doi/10.1145/2461328.2461340} }
Direct Trajectory Optimization of Rigid Body Dynamical Systems Through Contact

Michael Posa and Russ Tedrake

In The Workshop on the Algorithmic Foundations of Robotics (WAFR), 2012

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Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Most state-of-the-art techniques treat the discontinuous dynamics of contact as discrete modes and restrict the search for a complete path to a specified sequence through these modes. Here we present a novel method for trajectory planning through contact that eliminates the requirement for an a priori mode ordering. Motivated by the formulation of multi-contact dynamics as a Linear Complementarity Problem (LCP) for forward simulation, the proposed algorithm leverages Sequential Quadratic Programming (SQP) to naturally resolve contact constraint forces while simultaneously optimizing a trajectory and satisfying nonlinear complementarity constraints. The method scales well to high dimensional systems with large numbers of possible modes. We demonstrate the approach using three increasingly complex systems: rotating a pinned object with a finger, planar walking with the Spring Flamingo robot, and high speed bipedal running on the FastRunner platform.
@inproceedings{Posa2012, address = {Cambridge, MA}, author = {Posa, Michael and Tedrake, Russ}, booktitle = {The Workshop on the Algorithmic Foundations of Robotics (WAFR)}, month = jun, pages = {527--542}, title = {{Direct Trajectory Optimization of Rigid Body Dynamical Systems Through Contact}}, year = {2012}, youtube = {pH1pDXnCBx4}, url = {https://link.springer.com/chapter/10.1007/978-3-642-36279-8_32} }

Ph.D. Theses

Controlling Contact Transitions for Dynamic Robots

William Yang

University of Pennsylvania, 2024

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Legged robots, robotic manipulators, and their combined embodiment as humanoid robots have received considerable attention across both academia and industry. However, with few notable exceptions, state-of-the-art demonstrations are significantly less dynamic than their biological counterparts. A formidable challenge towards achieving more dynamic robots lies within controlling contact interactions with their environment. Legged robots undergoing impacts experience near-instantaneous changes in their velocities, making accurate state estimation difficult and resulting in controller sensitivity to even small deviations in impact timing. Contact transitions are also challenging for robot manipulation due to the combinatorial complexity of planning across multiple contact modes. Frictional contact that often arises from dynamic manipulation further increases this planning complexity due to the introduction of additional contact modes and increased degree of underactuation. To address these limitations, this thesis proposes algorithmic and systems contributions to gracefully handle contact transitions for dynamic robots. First, we identify that uncertainties from impact events enter the system dynamics in a structured manner. We leverage this structure to propose a general modification to model-based feedback controllers, enabling selective robustness to impact uncertainty while maximally retaining control authority. We validate our approach on custom dynamic jumping and running controllers on the 3D bipedal robot, Cassie. Then, we examine dexterous dynamic manipulation through complex non-prehensile tasks that require considering the full spectrum of hybrid contact modes. We leverage recent advancements in contact-implicit MPC to generate contact-rich motion plans in real-time. We demonstrate, through careful integration of the MPC and low-level tracking controller, how contact-implicit MPC can be adapted to dynamic tasks. We perform two distinct tasks using the same model, notably without common aids such as reference trajectories or motion primitives, highlighting the generality of our approach.
@phdthesis{Yang_thesis, title = {Controlling Contact Transitions for Dynamic Robots}, author = {Yang, William}, year = {2024}, school = {University of Pennsylvania}, youtube = {1yPvr2eBExE} }
Addressing stiffness-induced challenges in modeling and identification of for rigid-body systems with friction and impact

Mathew Halm

University of Pennsylvania, 2023

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Imperfect, useful dynamical models have enabled significant progress in planning and controlling robotic locomotion and manipulation. Traditionally, these models have been physics-based, with accuracy relying upon manual calibration only feasible in laboratory environments. As robotics expands into complex real-world applications, models of unknown environments must instead be automatically fit to limited data. One major challenge is modeling frictional contact, especially during collisions involved in common robotics tasks. Rapid deformation under impact manifests as extreme sensitivity to initial conditions and material properties. Thus, even slight errors in state estimation and system identification can lead to significant prediction errors. Consequently, model inaccuracy or the sim-to-real gap often hinders the development of performant robotics algorithms. Physical models can be optimized using advanced techniques to overcome these challenges, but such methods have limited tractability when a large number of parameters are unknown. Furthermore, even given accurate parameters, roboticists often make inaccurate rigid-body approximations to reduce the computational burdens of physical simulation to meet faster-than-real-time requirements. An alternative black-box approach, in which models are learned from scratch, has attempted to address these issues for instance using deep neural networks (DNN’s). While DNNs in theory can capture any dynamical behavior, they empirically struggle with the stiff behaviors associated with contact. This dissertation instead focuses on scaling physical model identification to the high-dimensional setting and quantifying the limited accuracy of low-fidelity models. We consider rigid bodies undergoing rigid contact, for which infinite stiffness is represented as constrained optimization. By careful treatment of these constraints, we demonstrate that infinitely-stiff dynamics can be identified by optimizing a non-stiff objective. In conjunction, we use DNN’s in a white-box setting to model physical quantities, specifically reconstructing geometries from scratch. We then consider how rigid-body collision models lack the fidelity to correctly predict outcomes of nearly-simultaneous impacts—such as heel and toe strikes during a footstep. We develop a theoretical basis to capture partial knowl- edge of impact events as uncertain set-valued outcomes, and again use numerical optimization to compute approximations of such sets.
@phdthesis{Halm_thesis, title = {Addressing stiffness-induced challenges in modeling and identification of for rigid-body systems with friction and impact}, author = {Halm, Mathew}, year = {2023}, school = {University of Pennsylvania}, youtube = {KeiKVDfnd1w}, url = {https://repository.upenn.edu/handle/20.500.14332/59206} }
Control of Multi-Contact Systems via Local Hybrid Models

Alp Aydinoglu

University of Pennsylvania, 2023

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For many important tasks such as manipulation and locomotion, robots need to make and break contact with their environment. Although such multi-contact systems are common, they pose a significant challenge when it comes to analysis and control. This difficulty primarily stems from two key factors: 1) the rapid increase in the number of possible ways that a system can move or behave as the number of contacts increase (as a result of the hybrid structure), and 2) the inherent nonlinearities present in the system’s dynamics. As a result, for tasks which require initiating contact with the environment, many state-of-the-art methods struggle as the number of contacts increase. Considering the substantial difficulty of multi-contact problems, it’s only natural to raise the question: How can we solve such problems? In addressing this query, this thesis directs its attention toward the simplification of multi-contact problems. It does so by concentrating on local hybrid approximations, wherein the non-smooth, hybrid structure is retained, while linearizing the smooth elements within the dynamics to mitigate the complexities arising from nonlinearities. As a result, we focus on local hybrid models called linear complementarity systems which are simple models that qualitatively capture the underlying non-smooth, hybrid structure. Employing these local hybrid models, this thesis presents scalable and fast algorithmic solutions for challenging multi-contact problems. First, we present the first real-time MPC framework for multi-contact manipulation. The method is based on the alternating direction method of multipliers (ADMM) and is capable of high-speed reasoning over potential contact events. Then, we focus on utilizing tactile measurements for reactive control, which is very natural yet underexplored in the robotics community. We propose a control framework to design tactile feedback policies for multi-contact systems by exploiting the local complementarity structure of contact dynamics. This framework can close the loop on tactile sensors and it is non-combinatorial, enabling optimization algorithms to automatically synthesize provably stable control policies. Then, inspired by the connection between rectified linear unit (ReLU) activation functions and linear complementarity problems, we present a method to analyze stability of multi-contact systems in feedback with ReLU network controllers.
@phdthesis{Aydinoglu_thesis, title = {Control of Multi-Contact Systems via Local Hybrid Models}, author = {Aydinoglu, Alp}, year = {2023}, school = {University of Pennsylvania}, youtube = {dNuFWfYy6n4}, url = {https://repository.upenn.edu/handle/20.500.14332/59416} }
Toward High-Performance Simple Models of Legged Locomotion

Yu-Ming Chen

University of Pennsylvania, 2023

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This thesis addresses the challenges of model-based planning and control in legged locomotion, particularly the trade-off between computational speed and robot performance presented by different levels of model complexities. Full-order models, while rich in detail, are often too computationally demanding for real time planning, whereas conventional reduced-order models (ROMs) tend to oversimplify the dynamics, limiting overall performance potential. Our research focuses on a novel approach – the direct optimization of ROMs. This study seeks to enhance the performance of legged robots by automatically discovering the optimal ROMs that simultaneously deliver high robot performance while maintaining the necessary low dimensionality for real time planning applications. In this work, we formulate problems, provide algorithmic solutions, and deploy optimized ROMs on real robots. In the beginning of the thesis, we focus on a special case where we aim to find whole-body orientation coordinates (WBO) for legged robots that minimize angular momentum errors. This optimal WBO, while being a simple forward kinematic function, serves as a proxy of the real angular momentum and can be applied to complex tasks such as humanoid natural walking. In the second part of the thesis, we formulate a bilevel optimization problem to find optimal ROMs agnostic to controller choices, driven by user-defined objectives and task distributions. The results show substantial improvements in walking speed, ground slope adaptability and torque efficiency on a bipedal robot Cassie. Lastly, we cast the ROM optimization problem as a model-based reinforcement learning (RL) problem to further improve the model performance. This does not only show better performance improvements in experiment but also provide an easier way to implement model optimization and to realize the model performance on the robot.
@phdthesis{Chen_thesis, title = {Toward High-Performance Simple Models of Legged Locomotion}, author = {Chen, Yu-Ming}, year = {2023}, youtube = {yIpSiHy1MOY}, school = {University of Pennsylvania}, url = {https://repository.upenn.edu/entities/publication/2d7cf8dd-4497-4fd9-bb31-4d057ede716b} }
Optimization for control and planning of multi-contact dynamic motion

Michael Antonio Posa

Massachusetts Institute of Technology, 2017

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The fundamental promise of robotics centers on the ability to productively interact with a complex and changing world. Yet, current robots are largely limited to basic tasks in structured environments and act slowly and cautiously, afraid of incidental contact. In this thesis, we consider a class of control and planning problems for robots dynamically interacting with their environment. We address challenges that arise from non-smooth motions induced by contact, where discontinuities result from impact events and frictional forces. First, we examine the problem of trajectory optimization in contact-rich environments, and present two algorithms for synthesizing motions which make and break contact. The novel contactimplicit trajectory optimization algorithm lifts the problem and reasons over the set of possible contacts forces. In doing so, we eliminate the requirement for an a priori sequencing of the active contacts, and avoid explicit combinatorial complexity. We also introduce a direct collocation algorithm for optimizing high-accuracy trajectories, given an arbitrary contact schedule. This approach eliminates drift in the numerical integration of contact constraints, even when constraints result in closed kinematic chains and require non-minimal coordinates. Second, this thesis concerns questions of control synthesis and provable stability verification of a robot making and breaking contact. To verify stability, we introduce an algorithm for discovering polynomial Lyapunov functions, where the system dynamics include impacts and friction. We leverage the measure differential inclusion representation of non-smooth contact mechanics to efficiently optimize over Lyapunov functions in multi-contact settings. Since avoiding hazardous falls is a primary necessity for bipedal walking robots, we use similar tools to characterize the capabilities of multiple simple models used for balancing and push recovery. Using the notions of barrier functions and occupation measures, we explicitly bound the set of disturbances from which a robot can recover by balancing or stepping. The primary contributions of this thesis are computational in nature, and we heavily leverage modern approaches to both general nonlinear programming and convex optimization. Sums-of-squares, an approach to polynomial optimization utilizing semidefinite programming, plays a central role in our methods for formal stability analysis.
@phdthesis{Posa_thesis, title = {Optimization for control and planning of multi-contact dynamic motion}, author = {Posa, Michael Antonio}, year = {2017}, school = {Massachusetts Institute of Technology}, url = {https://dspace.mit.edu/handle/1721.1/111860}, youtube = {4SBDQ59xiic} }