HOMIE: Humanoid Loco-Manipulation with Isomorphic Exoskeleton Cockpit

Qingwei Ben, Feiyu Jia, Jia Zeng, Junting Dong, Dahua Lin, Jiangmiao Pang

Keywords: Humanoid Robots, Robot Learning: Reinforcement Learning, Loco-Manipulation, Teleoperation

TL;DR: We propose a system consists of isomorphic exoskeleton cockpit and loco-manipulation policy, which helps a single operator teleoperate humanoids to perform diverse loco-manipulation tasks, with cockpit giving commands and policy driving robots.

Abstract:

Generalizable humanoid loco-manipulation poses
significant challenges, requiring coordinated whole-body control
and precise, contact-rich object manipulation. To address this,
this paper introduces HOMIE, a semi-autonomous teleoperation
system that combines a reinforcement learning policy for body
control mapped to a pedal, an isomorphic exoskeleton arm for
arm control, and motion-sensing gloves for hand control, forming
a unified cockpit to freely operate humanoids and establish a
data flywheel. The policy incorporates novel designs, including
an upper-body pose curriculum, a height-tracking reward, and
symmetry utilization. These features enable the system to perform walking and squatting to specific heights while seamlessly
adapting to arbitrary upper-body poses. The exoskeleton, by
eliminating the reliance on inverse dynamics, delivers faster and
more precise arm control. The gloves utilize Hall sensors instead
of servos, allowing even compact devices to achieve 15 or more
degrees of freedom and freely adapt to any model of dexterous
hands. Compared to previous teleoperation systems, HOMIE
stands out for its exceptional efficiency, completing tasks in half
the time; its expanded working range, allowing users to freely
reach high and low areas as well as interact with any objects;
and its affordability, with a price of just $500.

Submission Number: 1