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

Fuente: arXiv
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Main Authors: Deichler, Anna, Wang, Siyang, Alexanderson, Simon, Beskow, Jonas
Format: Preprint
Published: 2025
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_version_ 1866911157551366144
author Deichler, Anna
Wang, Siyang
Alexanderson, Simon
Beskow, Jonas
author_facet Deichler, Anna
Wang, Siyang
Alexanderson, Simon
Beskow, Jonas
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2509_12880
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
Deichler, Anna
Wang, Siyang
Alexanderson, Simon
Beskow, Jonas
Robotics
Human-Computer Interaction
Machine Learning
68T05, 68T40
I.2.9; I.2.6; H.5.2
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.
title Towards Context-Aware Human-like Pointing Gestures with RL Motion Imitation
topic Robotics
Human-Computer Interaction
Machine Learning
68T05, 68T40
I.2.9; I.2.6; H.5.2
url https://arxiv.org/abs/2509.12880