All News

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!
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.
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.
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.
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.
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!