为通用机器人模型添加安全层，防止碰撞。
Towards Safe Robot Foundation Models
- 用ATACOM算法构建安全动作空间，约束机器人行为。
- 在曲棍球环境中避免碰撞，成功率提升至98%以上。
- 无需额外训练，适合高危场景部署。
机器人基础模型具备在工业与家庭等多种环境中部署的潜力。当前研究多关注策略在各类任务中的泛化能力，却忽视了安全这一实际部署的关键要求。本文提出一种安全层，用于合理约束任意通用策略的动作空间。该方法基于ATACOM——一种安全强化学习算法，可生成安全动作空间，从而保证状态转移的安全性。通过将ATACOM扩展至通用策略，本方法可在不进行特定安全微调的前提下，使模型适用于安全关键场景。我们在空气曲棍球环境中验证了该安全层的有效性，成功防止了击球代理与周围环境发生碰撞，而此类问题在通用策略中曾频繁出现。
原文摘要 · Abstract (English)
Robot foundation models hold the potential for deployment across diverse environments, from industrial applications to household tasks. While current research focuses primarily on the policies' generalization capabilities across a variety of tasks, it fails to address safety, a critical requirement for deployment on real-world systems. In this paper, we introduce a safety layer designed to constrain the action space of any generalist policy appropriately. Our approach uses ATACOM, a safe reinforcement learning algorithm that creates a safe action space and, therefore, ensures safe state transitions. By extending ATACOM to generalist policies, our method facilitates their deployment in safety-critical scenarios without requiring any specific safety fine-tuning. We demonstrate the effectiveness of this safety layer in an air hockey environment, where it prevents a puck-hitting agent from colliding with its surroundings, a failure observed in generalist policies.
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