Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation

Jan Brüdigam¹, Ali Adeeb Abbas², Maks Sorokin², Kuan Fang², Brandon Hung², Maya Guru², Stefan Sosnowski¹, Jiuguang Wang², Sandra Hirche¹, Simon Le Cleac'h²
¹TU Munich, ²Boston Dynamics AI Institute
IEEE Robotics and Automation Letters (RA-L) 2024

Abstract

Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing planners are restricted to specific systems and often depend on specialized algorithms for using demonstrations. Therefore, we introduce a flexible motion planner tailored to dexterous and whole-body manipulation tasks. Our planner creates readily usable demonstrations for reinforcement learning algorithms, eliminating the need for additional training pipeline complexities. With this approach, we can efficiently learn policies for complex manipulation tasks, where traditional reinforcement learning alone only makes little progress. Furthermore, we demonstrate that learned policies are transferable to real robotic systems for solving complex dexterous manipulation tasks.

Approach

We combined reinforcement learning with sampling-based algorithms to solve contact-rich manipulation tasks. While sampling-based planners can quickly find successful trajectories for complex manipulation tasks, the solutions often lack robustness. We leveraged a reinforcement learning algorithm to enhance the robustness of a set of planner demonstrations, distilling them into a single policy that can handle variations and uncertainties in real-world scenarios.