Towards Safe Remote Manipulation: User Command Adjustment based on Risk Prediction for Dynamic Obstacles

Real-time remote manipulation requires careful operations by a user to ensure the safety of a robot, which is designed to follow user's commands, against dynamic obstacles. However, a user may give commands to a robot at the risk of collision with dynamic obstacles due to a user's unfamiliar control ability or unexpected situations. In this paper, we propose a risk-aware user command adjustment method to avoid potential collision with dynamic obstacles. Our method consists of a network that predicts the risk of dynamic obstacles and another network that synthesizes commands to avoid obstacles. Based on the predicted risk, our method decides an adjusted command between a user command and a command to avoid collisions. We evaluate our method in problems that face collisions with dynamic obstacles when following given commands and in problems with static obstacles. We show that our method improves safety against the risk of dynamic obstacles or follows user commands when there is no risk. We also demonstrate the feasibility of our method using the real fetch manipulator with seven-degrees-of-freedom.