Combining Learning-based Locomotion Policy with Model-based Manipulation for Legged Mobile Manipulators

• Published in: IEEE robotics and automation letters Vol. 7; no. 2; p. 1
• Main Authors: Ma, Yuntao, Farshidian, Farbod, Miki, Takahiro, Lee, Joonho, Hutter, Marco
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Costs; Deep learning; Dynamics; Generators; Legged locomotion; Legged Robots; Locomotion; Manipulators; Mobile Manipulation; Policies; Predictive control; Reinforcement Learning; Robot arms; Robot dynamics; Robots; Training; Trajectory
• DOI: 10.1109/LRA.2022.3143567
• ISSN: 2377-3766

Deep reinforcement learning produces robust locomotion policies for legged robots over challenging terrains. To date, few studies have leveraged model-based methods to combine these locomotion skills with the precise control of manipulators. Here, we incorporate external dynamics plans into learning-based locomotion policies for mobile manipulation. We train the base policy by applying a random wrench sequence on the robot base in simulation and add the noisified wrench sequence prediction to the policy observations. The policy then learns to counteract the partially-known future disturbance. The random wrench sequences are replaced with the wrench prediction generated with the dynamics plans from model predictive control to enable deployment. We show zero-shot adaptation for manipulators unseen during training. On the hardware, we demonstrate stable locomotion of legged robots with the prediction of the external wrench.