All News

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)
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.
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.”
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.
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
Oct 13, 2021 The lab has a new website. We’ve copied over some of our more recent news posts, but are largely starting fresh. Check it out!
Oct 1, 2021 When a robot interacts with the world, inevitably it will touch the wrong thing or slip instead of sticking. How should feedback work when the contact mode is changing? Linearization is not useful and hybrid (MIQP) problems cannot be solved in real-time. I’ve been thinking about this problem since the start of my Ph.D., and we’ve finally made some real progress! An ADMM algorithm, which we call Consensus Complementarity Control (C3), jointly optimizes over trajectory and contact mode for real-time MPC. https://arxiv.org/abs/2109.07076
Apr 21, 2021 When objects collide, small changes in initial conditions can lead to dramatically different outcomes (imagine a pool break). Rigid models capture this via non-uniqueness. Typically, model-based controllers optimistically ignore these possibilities, sometimes leading to poor behavior around impact events. Using differential inclusions and complementarity problems, we describe and compute the set of possible outcomes for multiple, frictional impacts and provide guarantees of existence and completeness. https://arxiv.org/abs/2103.15714
Mar 30, 2021 When a robot impacts its environment, it undergoes a large and rapid (though not quite instantaneous) change in velocity. Mode detection and state estimation in these brief periods are incredibly difficult, so it makes very little sense to apply feedback on these varying and imprecise velocity estimates. However, this uncertainty only applies to a subspace of velocities. In a new preprint, we project velocities onto an impact invariant subspace, preserving control authority in this subspace without spuriously reacting to impact-driven uncertainty.
http://arxiv.org/abs/2103.06907
Mar 30, 2021 Differentiable physics models enable learning contact dynamics for robotic systems, but at what cost? The underlying stiffness of contact poses a fundamental challenge to deep learning methods. Via numerical experiments learning ODEs for contact dynamics, we find that stiffness severely impacts (1) training error, (2) generalization error, and (3) data efficiency.

The theoretical underpinnings of these results are perhaps well known, arising from the high Lipschitz constants due to contact stiffness. However, given the rise of deep learning applied to differentiable physics models of contact, it’s important to keep in mind the limitations of these approximations. There’s a resulting fundamental trade-off between physical accuracy (for stiff robotic contact) and amenability to learning methods.

Learning on artificially soft contact models may not transfer to stiffer, real systems! https://arxiv.org/abs/2103.15406