Steering Generative Robot Policies with Lexicographic Preferences
cs.RO, cs.AI
Submitted: 2026-09-14
Updated: 2026-09-14
Comments: 11 pages
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- ViNT: A Foundation Model for Visual Navigation
- NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance
- Planning with Diffusion for Flexible Behavior Synthesis
- A General Framework for Inference-time Scaling and Steering of Diffusion Models
- VLS: Steering Pretrained Robot Policies via Vision-Language Models
- SafeDiffuser: Safe Planning with Diffusion Probabilistic Models
- SEAM: Smooth Execution of Action-Chunked Motion for Vision-Language-Action Policies
- Lexicographic Multi-Objective Reinforcement Learning
- STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving