Seeing-eye robots are very useful tools for guiding visually impaired people, potentially producing a huge societal impact given the low availability and high cost of real guide dogs. Although a few seeing-eye robot systems have already been demonstrated, none considered external tugs from humans, which frequently occur in a real guide dog setting. In this paper, we simultaneously train a locomotion controller that is robust to external tugging forces via Reinforcement Learning (RL), and an external force estimator via supervised learning. The controller ensures stable walking, and the force estimator enables the robot to respond to the external forces from the human. These forces are used to guide the robot to the global goal, which is unknown to the robot, while the robot guides the human around nearby obstacles via a local planner. Experimental results in simulation and on hardware show that our controller is robust to external forces, and our seeing-eye system can accurately detect force direction. We demonstrate our full seeing-eye robot system on a real quadruped robot with a blindfolded human. The video can be seen at our project page: https://bu-air-lab.github.io/guide_dog/

翻译：导盲机器人是帮助视障人士的重要工具，鉴于真实导盲犬的低可用性和高成本，该技术可能产生巨大的社会影响。尽管已有少数导盲机器人系统得到验证，但尚无系统考虑人类施加的外部拉力——这在真实导盲场景中频繁发生。本文通过强化学习（RL）同步训练对外部拉力具备鲁棒性的运动控制器，并通过监督学习训练外部力估计器。该控制器保证稳定行走，而力估计器使机器人能够响应人类施加的外力。这些外力被用于引导机器人向全局目标（机器人未知）行进，同时机器人通过局部规划器引导人类避开附近障碍物。仿真与硬件实验结果表明，我们的控制器对外力具有鲁棒性，且导盲系统可准确检测力的方向。我们在真实四足机器人上演示了完整导盲机器人系统，并由蒙眼人类参与测试。视频见项目主页：https://bu-air-lab.github.io/guide_dog/