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About this paper
Zhefeng Cao, Ben Liu, Shunpeng Yang, Sen Li, Wei Zhang, Hua Chen
Researchers from Southern University of Science and Technology and HKUST introduced a unified Transformer-based guided diffusion model capable of retargeting reference motions to multiple heterogeneous robotic embodiments. The framework generates kinematically feasible movements, adapts to diverse robot morphologies, and enables long-horizon whole-body tracking in simulation with competitive performance against baselines and minimal fine-tuning for new robots.
Key insights
- Representing robot skeletons as graphs and employing customized spatial attention allows the model to effectively capture and generalize across varied joint topologies and kinematic chains.
- Energy-based guidance within a diffusion framework offers a data-efficient method for robotic motion retargeting, overcoming the scarcity of paired robot motion datasets by providing task-specific supervision.
- Allocating dedicated attention heads to distinct conditions (joint axes, link lengths, limits) in a multi-conditional cross attention block improves the model's ability to integrate diverse semantic information.
- Both spatial and temporal attention are critical, with temporal attention demonstrating a more pronounced impact on learning coherent and robust motion features for generation.
Summary by alphaXiv.
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