用力觉反馈增强强化学习，让机器人在真实环境中更安全高效。
Reinforcement Learning for Robotic Safe Control with Force Sensing
- 引入力觉与触觉感知改进强化学习策略
- 仿真与真实环境下均提升操作安全性与效率
- 适合需要高可靠性交互的机器人应用场景
在非结构化环境中的复杂操作任务中，传统人工编码方法效果有限，而强化学习能生成更具通用性的控制策略。尽管强化学习表现优异，但其稳定性和可靠性难以保证，可能带来安全隐患。此外，从仿真到现实的迁移也常引发不可预测的情况。为提升机器人的安全性和可靠性，本文将力觉与触觉感知引入强化学习。力与触觉感知在机器人动态控制和人机交互中起关键作用。实验表明，基于力觉的强化学习方法对环境变化更具适应性，尤其在仿真到现实的迁移中表现更优。在物体推移任务中，该策略在仿真与真实场景下均展现出更高的安全性与效率，具备广泛应用于各类机器人的前景。
原文摘要 · Abstract (English)
For the task with complicated manipulation in unstructured environments, traditional hand-coded methods are ineffective, while reinforcement learning can provide more general and useful policy. Although the reinforcement learning is able to obtain impressive results, its stability and reliability is hard to guarantee, which would cause the potential safety threats. Besides, the transfer from simulation to real world also will lead in unpredictable situations. To enhance the safety and reliability of robots, we introduce the force and haptic perception into reinforcement learning. Force and tactual sensation play key roles in robotic dynamic control and human-robot interaction. We demonstrate that the force-based reinforcement learning method can be more adaptive to environment, especially in sim-to-real transfer. Experimental results show in object pushing task, our strategy is safer and more efficient in both simulation and real world, thus it holds prospects for a wide variety of robotic applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。