Learning Efficient Policies for Entangled Wire Harnesses

Wire harnesses are essential connecting components in the manufacturing industry but are challenging to automate in industrial tasks such as bin picking. They are long, flexible and tend to get entangledEntangled when randomly placed in a bin. This makes it difficult for the robot to grasp a single one in dense clutter. Besides, training or collecting data in simulationSimulation is challenging due to the difficulties in modeling the combination of deformable and rigid components for wire harnesses. In this work, instead of directly lifting wire harnesses, we propose to grasp and extract the target following a circle-like trajectory until it is untangled. We learn a policy from real-world data that can infer grasp and separation actions from visual observation. Our policy enables the robot to efficiently pick and separate entangledEntangled wire harnesses by maximizing success ratesSuccess ratesPerformance and reducing executionExecutionprediction model time. To evaluate our policy, we present a set of real-world experiments on picking wire harnesses. Our policy achieves an overall 84.6% success rate compared with 49.2% in baseline. We also evaluate the effectiveness of our policy under different clutter scenarios using unseen types of wire harnesses. Results suggest that our approach is feasible for handling wire harnesses in industrial bin picking. Supplementary material, code, and videos can be found at https://xinyiz0931.github.io/aspnet