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

Dec 14, 2022 How much can you accomplish with only a few minutes of data to learn from? Quite a bit! We use 4 minutes of experiential data to learn a model for robust real-time manipulation of a previously unknown object. Work led by Wanxin Jin, and supported by the Toyota Research Institute.

Dexterous manipulation, making and breaking frictional contact, is inherently hybrid, with thousands of possible modes. Fortunately, most of these are unnecessary for control. Here, we’re learning a task-relevant reduced-order hybrid model, limiting the number of hybrid modes. This builds on a bunch of our recent work on (1) data-efficient learning of multi-contact models (ContactNets and related papers) and (2) real-time MPC through contact. In this paper, we bridge these two by imbuing the model-learning process with task relevancy. Check out the project website and the paper draft https://arxiv.org/abs/2211.16657.
Nov 22, 2022 The last decade has seen tremendous progress in legged robots, driven by (among other things) optimization-based control. With Patrick Wensing, Yue Hu, Adrien Escande, Nicolas Mansard, and Andrea del Prete, we survey the field with an eye on what’s next Within the breadth of work in this area, we identify four main points of distinction.
  1. The choice of contact model, with implications on discontinuity or differentiability, and whether/how an algorithm must sequence the schedule of environmental contacts.
  2. The role of simplified models, which efficiently capture essential dynamic properties, in enabling real-time control and planning.
  3. The choice between numerical methods for optimal control (e.g. iLQR/DDP/collocation).
  4. Optimization-based (often QP) strategies for realizing motion planes via real-time feedback.
We hope this will serve as a useful introduction for both new and experienced roboticists, particularly those with new ideas in control and learning! Check out the preprint on arxiv–feedback is welcome!

https://arxiv.org/abs/2211.11644
Oct 25, 2022 The center of mass (CoM) position is the weight sum of each body’s position in a system, but is there an angular counterpart? Can we average each body’s orientation to get the ``angular center of mass” (ACoM)? Over the summer, Yu-Ming Chen worked with Boardwalk Robotics and IHMC, and has a new preprint out! In it, he introduces the ACoM in layman’s terms and shows an application to natural walking with the humanoid Nadia.

https://arxiv.org/abs/2210.08111
May 30, 2022 It was awesome hosting the robotics world last week! DAIR Lab students gave demos at the GRASP Tour and at the Convention Center, with some press from the Philadelphia Inquirer, Alp was an award finalist, Bibit and William presented recent results at workshops, and Michael served as Local Arrangements Chair. ICRA was the first in-person conference for most of the lab, and also the first in-person conference with lab alumni! Tianze, Yuhan, and Mihir were all spotted representing their current Ph.D. and career institutions.
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:
  • Tuesday, at 3:45 in Room 123 (TuB17, Optimization and Optimal Control II Session)
  • Wednesday, at 3:40 in Room 121 (WeAw2, Awards Session)
older news...


Lab Wiki (private)