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：导盲机器人是引导视障人士的实用工具，鉴于真实导盲犬的低可用性和高成本，其有望产生巨大的社会影响。尽管已有少数导盲机器人系统得到展示，但均未考虑实际导盲场景中频繁出现的人类牵拉外力。本文通过强化学习同时训练对牵拉外力具有鲁棒性的运动控制器，并通过监督学习训练外力估计器。控制器确保稳定行走，外力估计器使机器人能够响应人类施加的外部作用力。这些力用于引导机器人向全局目标移动（该目标对机器人未知），而机器人则通过局部规划器引导人类绕过附近障碍物。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，且导盲系统能精确检测力的方向。我们在真实四足机器人上结合蒙眼人类测试了完整的导盲机器人系统。项目页面视频请见：https://bu-air-lab.github.io/guide_dog/