A Novel Movement-supported HRI Framework for Humanoid Robots

Current research related to human-robot interaction (HRI) of bipedal humanoid robots often assumes that the robot is in a standing stationary state, i.e., the relative position of the robot does not change, and rarely considers the effect of lower limb movement on interaction. However, HRI in the real world does not assume a moving or stationary state of the robot, and the equilibrium perturbations caused by movement can prevent HRI from functioning properly. In this paper, we propose a movement supported humanoid robot interaction method that empowers the robot to move stably while achieving HRI. First, a reinforcement learning-based neural network is run offline to generate interaction actions that satisfy the equilibrium constraint and support movement, and then an intention recognition network is introduced to run the movement-supported HRI framework online. It is demonstrated that the training method proposed in this paper can enable a bipedal robot to achieve a variety of interactive actions while moving stably.