A Closed-Loop Shared Control Framework for Legged Robots

Abstract

Shared control, as a combination of human and robot intelligence, has been deemed as a promising direction towards complementing the perception and learning capabilities of legged robots. However, previous works on human-robot control for legged robots are often limited to simple tasks, such as controlling movement direction, posture, or single-leg motion, yet extensive training of the operator is required. To facilitate the transfer of human intelligence to legged robots in unstructured environments, this paper presents a user-friendly closed-loop shared control framework. The main novelty is that the operator only needs to make decisions based on the recommendations of the autonomous algorithm, without having to worry about operations or consider contact planning issues. Specifically, a rough navigation path from the operator is smoothed and optimized to generate a path with reduced traversing cost. The traversability of the generated path is assessed using fast Monte Carlo tree search (FastMCTS), which is subsequently fed back through an intuitive image interface and force feedback to help the operator make decisions quickly, forming a closed-loop shared control. The simulation and hardware experiments on a hexapod robot show that the proposed framework gives full play to the advantages of human-machine collaboration, and improves the performance in terms of learning time from the operator, mission completion time, and success rate than comparison methods.