Imitating Human Search Strategies for Assembly
This addresses the challenge of robot assembly in uncertain environments, offering a practical solution for industrial automation, though it is incremental as it builds on existing learning from demonstration methods.
The paper tackles the problem of teaching robots to perform search strategies for alignment tasks under position uncertainty by imitating human demonstrations, resulting in successful completion of 2D peg-in-hole and 3D socket tasks with few demonstrations.
We present a Learning from Demonstration method for teaching robots to perform search strategies imitated from humans in scenarios where alignment tasks fail due to position uncertainty. The method utilizes human demonstrations to learn both a state invariant dynamics model and an exploration distribution that captures the search area covered by the demonstrator. We present two alternative algorithms for computing a search trajectory from the exploration distribution, one based on sampling and another based on deterministic ergodic control. We augment the search trajectory with forces learnt through the dynamics model to enable searching both in force and position domains. An impedance controller with superposed forces is used for reproducing the learnt strategy. We experimentally evaluate the method on a KUKA LWR4+ performing a 2D peg-in-hole and a 3D electricity socket task. Results show that the proposed method can, with only few human demonstrations, learn to complete the search task.