Predicting Tactile Sensory Outcome of Physical Human-Robot Interaction Through Embodied Learning Strategy

Abstract

Estimation of robotic dynamic states in physical human-robot interaction (pHRI) is crucial for robots to handle real-world uncertainties. Since wearable tactile sensor arrays have been increasingly applied in robots, the tactile sensory outcomes (TSOs) during pHRIs would provide practical and ideal information about dynamic states of a robot as responses of its actions. In this work, an embodied learning strategy is introduced to promote the formation of a deep learning-based embodied haptic model (DL-EH), enabling robots to implicitly learn their tactile sensorimotor dynamics in pHRI. Functionally, DL-EH adopts a spatial-temporal assist module (embodied haptic embedding) to assist a compact forward predictor (multimodal mapping and sensory prediction) in learning TSO prediction. Then, it can realize an end-to-end real-time TSO prediction of robot actions in pHRI based on current tactile signals without historical sequences. In real-world experiments, the effectiveness of DL-EH is demonstrated, especially in long-term TSO predictions. The generalization test is performed to validate DL-EH’s generality in new pHRI scenarios. Finally, a robotic framework via the proposed method is proposed for proof-of-concept in the pHRI application.