Learning Variable Admittance Control for Human-Robot Collaborative Manipulation

Human-robot collaboration has garnered significant attention in the manufacturing industry due to its potential for optimizing the strengths of both human operators and robots. In this study, we present a novel variable admittance control method based on iterative learning for collaborative manipulation, aiming to enhance operational performance. This proposed method enables the adjustment of admittance to meet task requirements without the need for heuristic designs of admittance modulation strategies. Furthermore, the incorporation of dynamic time warping in human operational detection assists in mitigating the learning performance decline caused by fluctuations in human operations. To validate the effectiveness of our approach, we conducted extensive experiments. The results of these experiments highlight that the proposed method enhances human-robot collaborative manipulation performance compared to conventional methods. This approach also exhibits the potential for addressing complex tasks that are typically influenced by diverse human factors, including skill level and intention.