Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity
This work addresses the challenge of real-time global optimization over contact sequences for dexterous manipulation, offering a practical solution for robots to autonomously explore and exploit contact interactions.
The paper presents a novel contact-implicit MPC approach that combines global sampling of end-effector locations with local complementarity-based control, enabling real-time dexterous manipulation of non-convex objects. The method achieves globally-informed contact-rich behaviors without requiring precomputed contact sequences.
To achieve general-purpose dexterous manipulation, robots must rapidly devise and execute contact-rich behaviors. Existing model-based controllers are incapable of globally optimizing in real-time over the exponential number of possible contact sequences. Instead, recent progress in contact-implicit control has leveraged simpler models that, while still hybrid, make local approximations. However, the use of local models inherently limits the controller to only exploit nearby interactions, potentially requiring intervention to richly explore the space of possible contacts. We present a novel approach which leverages the strengths of local complementarity-based control in combination with low-dimensional, but global, sampling of possible end-effector locations. Our key insight is to consider a contact-free stage preceding a contact-rich stage at every control loop. Our algorithm, in parallel, samples end effector locations to which the contact-free stage can move the robot, then considers the cost predicted by contact-rich MPC local to each sampled location. The result is a globally-informed, contact-implicit controller capable of real-time dexterous manipulation. We demonstrate our controller on precise, non-prehensile manipulation of non-convex objects using a Franka Panda arm. Project page: https://approximating-global-ci-mpc.github.io