Partial Motion Imitation for Learning Cart Pushing with Legged Manipulators
Mili Das, Morgan Byrd, Donghoon Baek, and Sehoon Ha

TL;DR
This paper introduces a partial imitation learning method enabling legged robots to perform stable cart pushing by transferring learned locomotion styles and imitating lower-body motions, validated in simulation environments.
Contribution
The work presents a novel partial imitation learning approach that transfers locomotion skills to cart manipulation, improving stability and accuracy over baselines.
Findings
The learned policy successfully pushes a cart along diverse trajectories.
The method transfers effectively from IsaacLab to MuJoCo environments.
It outperforms several baseline approaches in stability and precision.
Abstract
Loco-manipulation is a key capability for legged robots to perform practical mobile manipulation tasks, such as transporting and pushing objects, in real-world environments. However, learning robust loco-manipulation skills remains challenging due to the difficulty of maintaining stable locomotion while simultaneously performing precise manipulation behaviors. This work proposes a partial imitation learning approach that transfers the locomotion style learned from a locomotion task to cart loco-manipulation. A robust locomotion policy is first trained with extensive domain and terrain randomization, and a loco-manipulation policy is then learned by imitating only lower-body motions using a partial adversarial motion prior. We conduct experiments demonstrating that the learned policy successfully pushes a cart along diverse trajectories in IsaacLab and transfers effectively to MuJoCo. We…
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