LAUSR

In this letter, we developed a novel learning framework from physical human-robot interactions. Owing to human domain knowledge, such interactions can be useful for facilitation of learning. However, applying numerous interactions for training data might place a burden on human users, particularly in real-world applications. To address this problem, we propose formulating this as a model-based reinforcement learning problem to reduce errors during training and increase robustness. Our key idea is to develop 1) an advisory and adversarial interaction strategy and 2) a human-robot interaction model to predict each behavior. In the advisory and adversarial interactions, a human guides and disturbs the robot when it moves in the wrong and correct directions, respectively. Meanwhile, the robot tries to achieve its goal in conjunction with predicting the human's behaviors using the interaction model. To verify the proposed method, we conducted peg-in-hole experiments in a simulation and real-robot environment with human participants and a robot, which has an underactuated soft wrist module. The experimental results showed that our proposed method had smaller position errors during training and a higher number of successes than the baselines without any interactions and with random interactions.