Encoding Predictability and Legibility for Style-Conditioned Diffusion Policy
Striking a balance between efficiency and transparent motion is a core challenge in human-robot collaboration, as highly expressive movements often incur unnecessary time and energy costs. In collaborative environments, legibility allows a human observer a better understanding of the robot's actions, increasing safety and trust. However, these behaviors result in sub-optimal and exaggerated trajectories that are redundant in low-ambiguity scenarios where the robot's goal is already obvious. To address this trade-off, we propose Style-Conditioned Diffusion Policy (SCDP), a modular framework that constrains the trajectory generation of a pre-trained diffusion model toward either legibility ...
| Authors | Adrien Jacquet Crétides, Mouad Abrini, Hamed Rahimi et al. |
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| Primary category | cs.RO |
| Published | 17 March 2026 |
| Last updated at source | 6 May 2026 |
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