BayesFP provides a unified retraining-free sampler for diffusion and flow policies by casting constrained trajectory generation as posterior sampling via an extended Feynman-Kac corrector.
Inference-time policy steering through human interactions
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Flow control steers VLA models with real-time user inputs to achieve higher success rates and faster task completion while maintaining action quality.
A single constant initial noise vector found by Monte-Carlo search improves frozen diffusion and flow-matching robot policies on 46 of 51 tasks, by up to 55% absolute success rate.
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.
citing papers explorer
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BayesFP: Posterior Estimation for Flow-Based Policies via Feynman-Kac Sampling
BayesFP provides a unified retraining-free sampler for diffusion and flow policies by casting constrained trajectory generation as posterior sampling via an extended Feynman-Kac corrector.
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Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs
Flow control steers VLA models with real-time user inputs to achieve higher success rates and faster task completion while maintaining action quality.
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You've Got a Golden Ticket: Improving Generative Robot Policies With A Single Noise Vector
A single constant initial noise vector found by Monte-Carlo search improves frozen diffusion and flow-matching robot policies on 46 of 51 tasks, by up to 55% absolute success rate.
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DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.