A Robot Human‐Like Learning Framework Applied to Unknown Environment Interaction

Abstract

Learning from demonstration (LfD) is one of the promising approaches for fast robot programming. Most learning systems learn both movements and stiffness profiles from human demonstrations. However, they rarely consider the unknown environment interaction. In this paper, a robot human-like learning framework is proposed, where it can learn human skills through demonstration and complete the interaction task with an unknown environment. Firstly, the desired trajectory was generated by dynamic movement primitive (DMP) based on human demonstration. Then, an adaptive optimal admittance control scheme was employed to interact with environments with the reference adaptation method. Finally, the experimental study was conducted, and the effectiveness of the framework proposed in this paper was verified via a group of curved surface wiping experiments on a balloon with unknown model parameters.

Anthropomorphic motion planning for multi-degree-of-freedom arms

“…The main factors affecting the generalization ability are the unknown environment and the targets. Learning reference inputs through DMP algorithm and adaptive optimal admittance control method can effectively improve the robot’s ability to interact with unknown environment ( Xue et al, 2022 ). Compared to other algorithms, the traditional DMP algorithm has excellent generalization and anti-interference capabilities ( Gong et al, 2020 ).…”