Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
This work addresses the generation of human-like pointing gestures for robots, which is an incremental improvement over prior focus on recognition.
The paper tackled the problem of generating human-like pointing gestures for robots by introducing a motion capture dataset and using reinforcement learning with motion imitation to train policies that balance precision and natural dynamics, achieving context-aware pointing behaviors in simulation.
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.