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Learning Quadrupedal Locomotion with Impaired Joints Using Random Joint Masking

2024-03-01 09:37:30
Mincheol Kim, Ukcheol Shin, Jung-Yup Kim

Abstract

Quadrupedal robots have played a crucial role in various environments, from structured environments to complex harsh terrains, thanks to their agile locomotion ability. However, these robots can easily lose their locomotion functionality if damaged by external accidents or internal malfunctions. In this paper, we propose a novel deep reinforcement learning framework to enable a quadrupedal robot to walk with impaired joints. The proposed framework consists of three components: 1) a random joint masking strategy for simulating impaired joint scenarios, 2) a joint state estimator to predict an implicit status of current joint condition based on past observation history, and 3) progressive curriculum learning to allow a single network to conduct both normal gait and various joint-impaired gaits. We verify that our framework enables the Unitree's Go1 robot to walk under various impaired joint conditions in real-world indoor and outdoor environments.

Abstract (translated)

四足机器人凭借其敏捷的运动能力在各种环境中发挥了关键作用,从结构化的环境到复杂的恶劣地形。然而,由于外部事故或内部故障,这些机器人很容易失去运动功能。在本文中,我们提出了一个新颖的深度强化学习框架,以实现受伤关节的四足机器人行走。该框架包括三个组件:1)用于模拟受损关节场景的随机关节遮蔽策略,2)用于根据过去观察历史预测当前关节状态的关节状态估计器,3)用于允许单个网络进行正常步态和各种关节损伤步态的渐进式学习。我们验证,我们的框架使单元树Go1机器人能够在现实世界的室内和室外环境中以各种损伤关节条件行走。

URL

https://arxiv.org/abs/2403.00398

PDF

https://arxiv.org/pdf/2403.00398.pdf


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