A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control

Abstract

Due to their ability to adapt to different terrains, quadruped robots have drawn much attention in the research field of robot learning. Legged mobile manipulation, where a quadruped robot is equipped with a robotic arm, can greatly enhance the performance of the robot in diverse manipulation tasks. Several prior works have investigated legged mobile manipulation from the viewpoint of control theory. However, modeling a unified structure for various robotic arms and quadruped robots is a challenging task. In this paper, we propose a unified framework disturbance predictive control where a reinforcement learning scheme with a latent dynamic adapter is embedded into our proposed low-level controller. Our method can adapt well to various types of robotic arms with a few random motion samples and the experimental results demonstrate the effectiveness of our method.

Legged robots for object manipulation: A review

“…This can explain why grasping an object is harder for bipedal robots, since their support polygons are relatively small, only formed by two legs, and the mass of the torso and arms can easily shift the CoM outside of such a small area (Kim et al, 2021). Adding more legs can help mitigate this problem, thus fueling increasing research in quadruped-and hexapodbased manipulation (Kalouche et al, 2015;Fang and Gao, 2018;van Dam et al, 2022;Xin et al, 2022;Yao et al, 2022). Conversely, we note that pushing against a large object can actually increase the robot's stability, if the object can help support some of the robot's weight.…”