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Achieving up to 98% higher success rates in contact-rich robotic tasks by optimizing memory and computation through implicit differentiation.
Bipedal robots can now plan 30-step trajectories in under 125ms, thanks to a continuous A* search that smokes discretized methods and MIP solvers.
Achieve faster, more accurate robot system identification by embedding physical constraints directly into a Bayesian estimation framework, leading to measurable improvements in model predictive control tracking performance.