CRII: RI: Practical Algorithms for Robust Feedback Motion Planning Through Contact

Harvard University, Cambridge MA

Investigators

Abstract

Despite significant progress in robot motion planning and control, modern robots still struggle to operate robustly in the presence of unplanned disturbances, state uncertainty, and model errors. Indeed, the recent DARPA Robotics Challenge dramatically demonstrated that some of the world's most advanced robots still minimize contact with their environments and fail often in realistic operating scenarios. This creates a significant barrier to unleashing robots into critical disaster response, exploration, and industrial applications. Algorithms that explicitly reason about robustness require a coupling of motion planning and feedback design, in which the system's closed-loop response to disturbance sources is optimized. Due to the often heavy computational demands of solving such problems, their application to modern field robots has so far been limited. The research objective of this proposal is to address the theoretical, computational, and applied challenges of robust motion planning for robots with nonlinear dynamics and state and input constraints, including constraints arising from frictional contact. The algorithms under consideration in this research will build on direct trajectory optimization methods to simultaneously 1) support complex state constraints and rigid-body contacts and 2) exploit mathematical structure in disturbance and feedback controller sets to construct computationally-efficient algorithms. The ability of these algorithms to improve stability in tasks of broad interest, such as bipedal walking, will be investigated. Algorithms will be evaluated against state-of-the-art methods in locomotion and manipulation experiments on physical robots.