Fast Contact-Implicit Model-Predictive Control
This work addresses the challenge of real-time, contact-rich control for robotic systems, enabling more adaptive and robust behaviors in dynamic environments, though it is incremental in extending linear MPC to contact settings.
The paper tackles the problem of controlling robots that frequently make and break contact with their environment by developing a contact-implicit model-predictive control (CI-MPC) approach, which achieves real-time solution rates and demonstrates the ability to generate and track non-periodic behaviors in hardware experiments on a quadrupedal robot, with robustness to model mismatch and disturbances across various simulated systems.
We present a general approach for controlling robotic systems that make and break contact with their environments. Contact-implicit model predictive control (CI-MPC) generalizes linear MPC to contact-rich settings by utilizing a bi-level planning formulation with lower-level contact dynamics formulated as time-varying linear complementarity problems (LCPs) computed using strategic Taylor approximations about a reference trajectory. These dynamics enable the upper-level planning problem to reason about contact timing and forces, and generate entirely new contact-mode sequences online. To achieve reliable and fast numerical convergence, we devise a structure-exploiting interior-point solver for these LCP contact dynamics and a custom trajectory optimizer for the tracking problem. We demonstrate real-time solution rates for CI-MPC and the ability to generate and track non-periodic behaviours in hardware experiments on a quadrupedal robot. We also show that the controller is robust to model mismatch and can respond to disturbances by discovering and exploiting new contact modes across a variety of robotic systems in simulation, including a pushbot, planar hopper, planar quadruped, and planar biped.