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

2025-09-16

AI Summary

Existing robotic pointing gesture generation methods primarily focus on target identification, lacking unified modeling of contextual awareness and human-like naturalness. Method: This paper proposes a context-aware, human-like pointing gesture generation framework that integrates reinforcement learning with motion imitation. We first construct a comprehensive motion-capture dataset covering diverse pointing styles and targets; then model motion priors from this data and jointly optimize, in simulation, for both accuracy (target localization error) and naturalness (kinematic plausibility and posture adaptability). Contribution/Results: Experiments demonstrate that our method dynamically adjusts full-body pose according to spatial target location and environmental context, achieving millimeter-level pointing accuracy while significantly enhancing gesture naturalness and human–robot interaction fluency—marking a critical transition from “recognition” to “generation” in robotic pointing behavior.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Generating human-like pointing gestures for robots
• Maximizing pointing precision using reinforcement learning
• Balancing task performance with natural motion dynamics

Innovation

• Reinforcement learning with motion imitation
• Human-like pointing gesture generation
• Context-aware pointing balancing precision dynamics