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 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. |
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| Nov 19, 2021 |
DAIR Ph.D. student Matt Halm gave the Penn GRASP SFI seminar a few weeks ago. Check out the talk, “Physics-inspired learning for discontinuous contact dynamics.”
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| Oct 21, 2021 |
We’re excited that the paper “Stabilization of Complementarity Systems via Contact-Aware Controllers,” led by Alp Aydinoglu, has been accepted for publication in IEEE Transactions on Robotics (TRO). In it, we solve bilinear matrix inequalities to synthesize control policies that explicitly use measured state and force for feedback. Check out the video where the controller stabilizies a cart-pole that slams into nearby walls!
https://arxiv.org/abs/2008.02104 |
| Oct 20, 2021 |
Michael gave the Robotics Seminar at MIT not too long ago. The talk was recorded and is publically available.
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| Oct 14, 2021 | 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 |
Lab Wiki (private)