A Human–Robot Shared Strategy Considering Human Behavior Uncertainty and Interaction Disagreements

Physical human-robot interaction (pHRI) systems can combine the robot’s precise control ability with the human’s decision-making ability. However, the robot’s inability to fully understand human intentions may inevitably cause disagreements during the interaction process. This work develops a novel human-robot shared strategy to reduce human-robot disagreements and enhance the robot assistance level. We present a diffusion model to predict human intention using as inputs the past trajectories, the past human forces, the past robot forces and task environment information. Considering the uncertainty of human behavior and prediction model, we model the pHRI system as a Linear Quadratic Gaussian (LQG) stochastic system, incorporating the uncertainty into the cost function of the pHRI system. Finally, we propose an adaptive impedance control strategy based on human-robot disagreements to ensure that the robot can assist human movement to the greatest extent. Experiments are conducted on a Sawyer robot to verify the effectiveness of the proposed method. The results show that the proposed strategy enables the robot to adapt to the human based on human-robot disagreements and enhance the assistance level.