Steering Generative Robot Policies with Lexicographic Preferences
Pretrained generative robot policies can produce effective behaviors across diverse environments, but deployment can lead to requirements and preferences that may not have been represented during training. Furthermore, at deployment, an operator, user, or application may assign these requirements and preferences a priority order that can vary across deployments. For example, embodiment-specific feasibility constraints may need to be satisfied first, while user-specific preferences guide behavior among the feasible options. We show that a frozen generative robot policy---based on either diffusion or flow matching---can be steered at inference time to respect such lexicographically ordered ...
| Authors | Yixuan Jia, Jonathan P. How |
|---|---|
| Primary category | cs.RO |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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