Long-Horizon Torque-Limited Planning through Contact using Discrete Search and Continuous Optimization

Ramkumar Natarajan, Garrison L Johnston, Nabil Simaan, Maxim Likhachev, Howie Choset

Keywords: Motion Planning, Manipulation Planning, Contact rich manipulation, Kinodynamic Planning, Global Trajectory Optimization

TL;DR: Global Contact Rich Automatic Discovery of Motion Plans

Abstract: By bracing against the environment robots can expand their reachable workspace that would otherwise be inaccessible due to exceeding actuator torque limits and as well as accomplish tasks beyond their design specifications. As such, it is desirable to interact with the environment to explore new possibilities to complete a task. However, motion planning for complex contact-rich tasks requires reasoning through the permutations of different possible contact modes that grow exponentially with the number of contact points and links in the robot. To address this combinatorial problem, we developed INSAT that interleaves graph search to explore the manipulator joint configuration space with incremental trajectory optimizations seeded by neighborhood solutions to find a dynamically feasible trajectory through contact. In this paper, we present recent additions to the INSAT algorithm that improve its runtime performance. In particular, we propose Lazy INSAT with reduced optimization rejection that systematically procrastinates its calls to trajectory optimization while reusing feasible solutions that violate boundary constraints. The algorithm is evaluated on a heavy payload transportation task in simulation and on physical hardware. In simulation, we show that Lazy INSAT is able to discover solutions for tasks that cannot be accomplished within its design limits and without interacting with the environment. In comparison to executing the same trajectory without environment support, we show that the utilization of bracing contacts reduces the overall torque required to execute the trajectory.

Submission Number: 21