Dynamic Autonomy and Intelligent Robotics Lab
170B Towne Building, 220 S. 33rd Street, Philadelphia PA, 19104
Welcome to the Dynamic Autonomy and Intelligent Robotics (DAIR) Lab!
Our research centers on planning, control, and formal analysis of robots as they interact with the world. Whether a robot is assisting within the home, or operating in a manufacturing plant, the fundamental promise of robotics requires touching and affecting a complex environment in a safe and controlled fashion. We are focused on developing computationally tractable algorithms which enable robots to operate both dynamically and safely as they quickly maneuver through and interact with their environments.
Right now, we are particularly interested in understanding the interplay between the non-smooth dynamics of contact and numerical optimization, and then testing these techniques on both legged robots and robotic manipulation.
We are proud to be a group within the Penn Engineering GRASP Lab.
Recent Updates
| May 22, 2022 |
We’re excited to see everyone at ICRA this week! Alp’s paper, Real-Time Multi-Contact Model Predictive Control via ADMM, which was named a Finalist for Oustanding Dynamics and Control Paper, will be presented twice:
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| Apr 29, 2022 | Updating an older post, this paper was accepted to RA-L/IROS, congrats to Brian and Will! How well do modern robotics simulators reproduce impact dynamics? How important are appropriately tuned contact parameters for physical realism? We compared simulated trajectories against real impact data from a cube toss and Penn Cassie jumping (or more accurately, landing). Simulators faithfully capture near rigid impacts while struggling with elasticity. While accuracy in reproducing cube toss data is largely insensitive to contact parameters if the parameters are stiff enough, correct stiffness and damping are necessary for accurately reproducing Cassie trajectories. https://arxiv.org/abs/2110.00541 |
| Mar 1, 2022 | We had one paper accepted to ICRA 2022 Real-Time Multi-Contact Model Predictive Control via ADMM, and two accepted to L4DC 2022 Generalization Bounded Implicit Learning of Nearly Discontinuous Functions and Learning Linear Complementarity Systems. Congrats to Alp, Bibit, Wanxin and Matt! |
| Feb 28, 2022 | Michael will be giving a couple of talks soon, at UC Santa Barbara and the University of Toronto. The talk at Toronto will be live streamed on YouTube, check it out to learn some of the details on our latest work. |
| Dec 15, 2021 |
Implicit learning has shown a lot of promise, particularly for representing (near) discontinuous functions. For example, our recent work on ContactNets used implicit representations of geometry. Similarly, we’ve seen how unstructured, explicit approaches struggle with learning stiff or discontinuous functions. We’ve set out to better understand why (and when) implicit representations are useful. Most obviously, an implicit parameterization can better represent non-smoothness. Other authors have exploited this, for instance via embedding differentiable optimization into the learning process (Belbute-Peres et al., “End-to-end differentiable physics for learning and control”). However, this is only part of the story. If the underlying function to be learned is stiff or discontinuous, this stiffness ultimately manifests in the loss function. Instead, we’ve investigated ContactNets-inspired implicit losses which balance optimality of the embedded problem against prediction error, with better performance on near-discontinuous learning problems. In a new preprint, “Generalization Bounded Implicit Learning of Nearly Discontinuous Functions” by Bibit Bianchini et al., we show how this violation-implicit loss provably generalizes well to unseen data. The resulting loss landscape is well-conditioned (with low Lipschitz constants, despite the stiff underlying function). We also provably connect this loss to graph distance, a natural metric for evaluating steep or discontinuous functions. |
Lab Wiki (private)