TRACE: Typed Reference-Aware Conversational Editing for Humanoid Motion Diffusion
Language-conditioned humanoid motion generation enables users to create and refine robot movements through natural language.
ProofPaper ↗
Key points
- We present TRACE, a generation and editing framework for the Unitree G1 humanoid robot built around typed, measurable kinematic quantities.
- Our insight is to ground generation conditions in paired training motions and editing goals in the current motion.
- A language model learns to predict support, root, and hand events from motion-derived supervision; their time-aligned encodings complement the original text in a diffusion generator.
- Gravity-referenced states and trajectory--contact objectives improve kinematic consistency.
Sources (1)
- [1]TRACE: Typed Reference-Aware Conversational Editing for Humanoid Motion DiffusionarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 08:49 AM
Language-conditioned humanoid motion generation enables users to create and refine robot movements through natural language.
We present TRACE, a generation and editing framework for the Unitree G1 humanoid robot built around typed, measurable kinematic quantities.
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