Energy Approximated Dynamic Subattractor for Adjusting Obstacle Avoidance Trajectories.

Imitation learning is an important method for the human-robot skill transfer. However, ensuring that skills learned through imitation remain effective in different environments is a challenge. This article addresses the challenge by proposing a stable autonomous DS that can effectively handle obstacles and disturbances while maintaining trajectory accuracy. We introduce an energy-approximated dynamic subattractor (EADA) method that enhances disturbance resistance by dynamically selecting subattractors through Neum (an energy function derived from demonstration data). By combining velocity modulation algorithms with EADA, the system achieves global stability, precise obstacle avoidance, autonomous trajectory recovery, and rapid response. The proposed framework effectively handles complex scenarios, including environments with multiple obstacles, dynamic obstacles, and disturbances. We validate the proposed approach through simulations on the LASA dataset and real-world robotic experiments (both single-arm and dual-arm robots), demonstrating its effectiveness in achieving smooth and accurate obstacle avoidance trajectories with generalization capability.