PreferenceFlow: Test-Time Guidance of Flow-Matching Robot Policies from Human Interventions
cs.RO
Submitted: 2026-09-29
Updated: 2026-09-29
Terminology
Sources
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
- Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- World Action Models are Zero-shot Policies
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Flex-$\pi$: A Multi-Stream World-Action Model with Compute Flexibility
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- Steering Your Diffusion Policy with Latent Space Reinforcement Learning
- RL Token: Bootstrapping Online RL with Vision-Language-Action Models
- UniSteer: Unified Noise Steering for Efficient Human-Guided VLA Adaptation
- Improving Robotic Generalist Policies via Flow Reversal Steering
- FlowDAgger: Human-in-the-Loop Adaptation of Generative Robot Policies in Latent Space
- Steering Generative Robot Policies with Lexicographic Preferences
- Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
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