# Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation

arXiv: https://arxiv.org/abs/2509.12880 · HTML: https://mlsanity.com/paper/2509.12880/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 24 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 1 of 1)

- [Speech Gesture Generation from the Trimodal Context of Text, Audio, and Speaker Identity](https://mlsanity.com/paper/2009.02119/) (2020)

## Abstract (arXiv)

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.