Agile Catching with Whole-Body MPC and Blackbox Policy Learning
Authors: name: Saminda Abeyruwan; name: Alex Bewley; name: Nicholas M. Boffi; name: Krzysztof Choromanski; name: David D'Ambrosio; name: Deepali Jain; name: Pannag Sanketi; name: Anish Shankar; name: Vikas Sindhwani; name: Sumeet Singh; name: Jean-Jacques Slotine; name: Stephen Tu
Published: 2023-06-14 02:13:25

Abstract: We address a benchmark task in agile robotics: catching objects thrown at
high-speed. This is a challenging task that involves tracking, intercepting,
and cradling a thrown object with access only to visual observations of the
object and the proprioceptive state of the robot, all within a fraction of a
second. We present the relative merits of two fundamentally different solution
strategies: (i) Model Predictive Control using accelerated constrained
trajectory optimization, and (ii) Reinforcement Learning using zeroth-order
optimization. We provide insights into various performance trade-offs including
sample efficiency, sim-to-real transfer, robustness to distribution shifts, and
whole-body multimodality via extensive on-hardware experiments. We conclude
with proposals on fusing "classical" and "learning-based" techniques for agile
robot control. Videos of our experiments may be found at
https://sites.google.com/view/agile-catching