Humanoid Motion Scripting with Postural Synergies

2025-08-16

AI Summary

To address challenges in humanoid robot motion generation—including heavy reliance on reference motion data, difficulty in style transfer, and weak real-time adaptability—this paper proposes SynSculptor, a training-free framework. Methodologically, it introduces momentum-based segmentation for joint velocity trajectory analysis and employs PCA to extract momentum-driven coordination patterns across motion segments, constructing a stylized coordination library; it further integrates a motion–language Transformer to enable end-task-driven dynamic pose editing and real-time adaptation. Contributions include: (1) eliminating dependence on large-scale motion datasets for training; (2) the first incorporation of momentum features into coordination modeling, enhancing physical plausibility and naturalness; and (3) enabling style-controllable, task-responsive, and temporally smooth motion synthesis. Experiments show that generated motions closely match real human motions in momentum/kinetic energy deviation, foot-sliding ratio, and smoothness metrics, while maintaining real-time performance.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Computer Vision: Motion & Tracking

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Generating sequences of human-like motions for humanoid robots presents challenges in collecting and analyzing reference human motions, synthesizing new motions based on these reference motions, and mapping the generated motion onto humanoid robots. To address these issues, we introduce SynSculptor, a humanoid motion analysis and editing framework that leverages postural synergies for training-free human-like motion scripting. To analyze human motion, we collect 3+ hours of motion capture data across 20 individuals where a real-time operational space controller mimics human motion on a simulated humanoid robot. The major postural synergies are extracted using principal component analysis (PCA) for velocity trajectories segmented by changes in robot momentum, constructing a style-conditioned synergy library for free-space motion generation. To evaluate generated motions using the synergy library, the foot-sliding ratio and proposed metrics for motion smoothness involving total momentum and kinetic energy deviations are computed for each generated motion, and compared with reference motions. Finally, we leverage the synergies with a motion-language transformer, where the humanoid, during execution of motion tasks with its end-effectors, adapts its posture based on the chosen synergy. Supplementary material, code, and videos are available at https://rhea-mal.github.io/humanoidsynergies.io.

Problem

• Generating human-like motions for humanoid robots
• Analyzing and synthesizing motion using postural synergies
• Mapping generated motions onto humanoid robots effectively

Innovation

• Leverages postural synergies for motion scripting
• Uses PCA for extracting major motion synergies
• Integrates motion-language transformer for posture adaptation