LAUSR

Cable routing is a challenging task for robotic automation. To accomplish the task, it requires a high-level path planner to generate a sequence of cable configurations from the initial state to the target state and a low-level manipulation planner to plan the robot motion commands to transit between adjacent states. However, there are yet no proper representations to model the cable with the environment objects, impeding the design of both high-level path planning and low-level manipulation planning. In this letter, we propose a framework for cable routing with spatial representation. For high-level planning, by considering the spatial relations between the cable and the environment objects such as fixtures, the proposed method is able to plan a path from the initial state to the goal state in a graph. For low-level manipulation, multiple manipulation primitives are efficiently learned from human demonstration, to configure the cable to planned intermediate states leveraging the same spatial representation. We also implement a cable state estimator that robustly extracts the spatial representation from raw RGB-D images, thus completing the cable routing framework. We evaluate the proposed framework with various cables and fixture settings, and demonstrate that it outperforms some baselines in terms of reliability and generalizability.