Approximating Global Contact-Implicit MPC via Sampling and Local Complementarity

TL;DR

This paper introduces a novel contact-implicit MPC method that combines local complementarity-based control with global sampling to enable real-time dexterous manipulation of complex objects.

Contribution

It presents a new algorithm that integrates global sampling with local contact-implicit control for improved dexterous manipulation.

Findings

Successfully manipulated non-convex objects with a Franka Panda arm.

Achieved real-time control capable of exploring diverse contact interactions.

Demonstrated the effectiveness of global sampling in contact-rich scenarios.

Abstract

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