用强化学习实现全向轮椅的智能共控，更顺滑安全。
Shared Control of Holonomic Wheelchairs through Reinforcement Learning
- 基于强化学习，将2D输入转为3D运动指令
- 仿真中实现无碰撞导航，平滑性优于传统方法
- 首次实现在真实轮椅上的强化学习共控
智能电动轮椅可通过共控机制提升用户体验。现有方法在非全向机器人中展现了安全性优势，但对全向系统常导致用户感知不自然，未能发挥全向移动潜力。本文提出一种基于强化学习的方法，接收二维用户输入并输出三维运动指令，兼顾用户舒适度与认知负荷。模型在Isaac Gym中训练，在Gazebo中测试，对比不同RL架构与奖励函数，评估指标涵盖认知负荷与用户舒适度。结果表明，该方法可实现无碰撞导航，智能调整轮椅朝向，平滑性优于或媲美此前非学习型方法。进一步完成从仿真到现实的迁移，据我们所知，首次实现强化学习驱动的全向移动平台真实世界共控。
原文摘要 · Abstract (English)
Smart electric wheelchairs can improve user experience by supporting the driver with shared control. State-of-the-art work showed the potential of shared control in improving safety in navigation for non-holonomic robots. However, for holonomic systems, current approaches often lead to unintuitive behavior for the user and fail to utilize the full potential of omnidirectional driving. Therefore, we propose a reinforcement learning-based method, which takes a 2D user input and outputs a 3D motion while ensuring user comfort and reducing cognitive load on the driver. Our approach is trained in Isaac Gym and tested in simulation in Gazebo. We compare different RL agent architectures and reward functions based on metrics considering cognitive load and user comfort. We show that our method ensures collision-free navigation while smartly orienting the wheelchair and showing better or competitive smoothness compared to a previous non-learning-based method. We further perform a sim-to-real transfer and demonstrate, to the best of our knowledge, the first real-world implementation of RL-based shared control for an omnidirectional mobility platform.
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