用自监督学习提升触觉强化学习，让机器人更灵巧地操作物体。
Enhancing Tactile-based Reinforcement Learning for Robotic Control
- 设计自监督学习框架，利用稀疏二值触觉信号增强感知。
- 在球弹跳和宝珠旋转任务中表现超越人类水平的灵巧性。
- 分离记忆模块可提升性能，适合做触觉操控研究的团队参考。
实现安全可靠的现实世界机器人操作，需让智能体超越视觉，融入触觉感知以弥补感官缺陷并减少对理想状态信息的依赖。尽管触觉感知潜力巨大，其在强化学习中的效果仍不稳定。为此，我们开发了自监督学习（SSL）方法，更有效地利用触觉观测，聚焦于本体感知与稀疏二值接触信号的可扩展组合。实验表明，稀疏二值触觉信号对灵巧操作至关重要，尤其在本体感知无法捕捉的解耦机器人-物体运动中。我们的智能体在复杂接触任务（如球弹跳与宝珠旋转）中达到超人级灵巧性。此外，发现将SSL记忆与在线策略记忆解耦可进一步提升性能。我们发布了机器人触觉奥林匹克（RoTO）基准，用于标准化并推动触觉操控研究的发展。
原文摘要 · Abstract (English)
Achieving safe, reliable real-world robotic manipulation requires agents to evolve beyond vision and incorporate tactile sensing to overcome sensory deficits and reliance on idealised state information. Despite its potential, the efficacy of tactile sensing in reinforcement learning (RL) remains inconsistent. We address this by developing self-supervised learning (SSL) methodologies to more effectively harness tactile observations, focusing on a scalable setup of proprioception and sparse binary contacts. We empirically demonstrate that sparse binary tactile signals are critical for dexterity, particularly for interactions that proprioceptive control errors do not register, such as decoupled robot-object motions. Our agents achieve superhuman dexterity in complex contact tasks (ball bouncing and Baoding ball rotation). Furthermore, we find that decoupling the SSL memory from the on-policy memory can improve performance. We release the Robot Tactile Olympiad (RoTO) benchmark to standardise and promote future research in tactile-based manipulation. Project page: https://elle-miller.github.io/tactile_rl
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