PvP: 通过体感优先对比表示实现数据高效的类人机器人学习
PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations
AI总结：
PvP通过体感优先对比学习提升类人机器人学习的样本效率和性能。
摘要：
实现高效且鲁棒的全身控制（WBC）对于使类人机器人在动态环境中执行复杂任务至关重要。尽管强化学习（RL）在这一领域取得了成功，但其样本效率低下仍是一个重大挑战，原因在于类人机器人的复杂动态和部分可观测性。为了解决这一限制，我们提出了PvP，一种利用本体感觉与优先状态之间内在互补性来学习紧凑且任务相关的潜在表示的体感优先对比学习框架。PvP无需手工设计的数据增强即可学习紧凑且任务相关的潜在表示，从而加快并稳定策略学习。为了支持系统评估，我们开发了SRL4Humanoid，第一个统一且模块化的框架，提供了高质量的代表性状态表示学习（SRL）方法的实现，用于类人机器人学习。在LimX Oli机器人上进行的广泛实验表明，PvP在速度跟踪和运动模仿任务中相比基线SRL方法显著提高了样本效率和最终性能。我们的研究进一步提供了将SRL与RL结合用于类人WBC的实用见解，为数据高效的类人机器人学习提供了有价值的指导。
英文摘要：
Achieving efficient and robust whole-body control (WBC) is essential for enabling humanoid robots to perform complex tasks in dynamic environments. Despite the success of reinforcement learning (RL) in this domain, its sample inefficiency remains a significant challenge due to the intricate dynamics and partial observability of humanoid robots. To address this limitation, we propose PvP, a Proprioceptive-Privileged contrastive learning framework that leverages the intrinsic complementarity between proprioceptive and privileged states. PvP learns compact and task-relevant latent representations without requiring hand-crafted data augmentations, enabling faster and more stable policy learning. To support systematic evaluation, we develop SRL4Humanoid, the first unified and modular framework that provides high-quality implementations of representative state representation learning (SRL) methods for humanoid robot learning. Extensive experiments on the LimX Oli robot across velocity tracking and motion imitation tasks demonstrate that PvP significantly improves sample efficiency and final performance compared to baseline SRL methods. Our study further provides practical insights into integrating SRL with RL for humanoid WBC, offering valuable guidance for data-efficient humanoid robot learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
