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

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