Steering Generative Robot Policies with Lexicographic Preferences
Quick summary
arXiv:2609.15014v1 Announce Type: cross Abstract: 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
Key takeaways
- arXiv:2609.15014v1 Announce Type: cross Abstract: 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.
Why it matters
The importance of “Steering Generative Robot Policies with Lexicographic Preferences” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
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