Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
arXiv:2509.12880
Abstract
Pointing is a key mode of interaction with robots, yet most prior work has focused on recognition rather than generation. We present a motion capture dataset of human pointing gestures covering diverse styles, handedness, and spatial targets. Using reinforcement learning with motion imitation, we train policies that reproduce human-like pointing while maximizing precision. Results show our approach enables context-aware pointing behaviors in simulation, balancing task performance with natural dynamics.
Presented at the Context-Awareness in HRI (CONAWA) Workshop, ACM/IEEE International Conference on Human-Robot Interaction (HRI 2022), March 7, 2022