TouchGuide improves contact-rich robot manipulation by steering diffusion or flow-matching visuomotor policies with tactile feasibility scores from a contrastively trained Contact Physical Model.
Streaming flow policy: Simplifying diffusion/flow- matching policies by treating action trajectories as flow trajectories
5 Pith papers cite this work. Polarity classification is still indexing.
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FEC conditions policies on LLM-guided short-horizon future videos via a three-stage pipeline, yielding performance gains for BC+RL over no-future baselines on RoboCasa and CALVIN while mismatched futures degrade results.
Q2RL extracts Q-values from a BC policy and applies Q-gating to enable efficient offline-to-online RL, outperforming baselines on D4RL/robomimic tasks and achieving up to 100% success on real-robot manipulation in 1-2 hours.
MoRE improves robot policy success rates by 44 percentage points by distilling mode redirection into weights, matching filtered retraining performance without inference overhead.
A novel diffusion variant accelerates minimum-time planning for redundant dual-arm robots by replacing gradient-based solving of the nonconvex high-level problem with probabilistic sampling, yielding 35x faster runtime and 34% less path error.
citing papers explorer
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TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance
TouchGuide improves contact-rich robot manipulation by steering diffusion or flow-matching visuomotor policies with tactile feasibility scores from a contrastively trained Contact Physical Model.
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LLM-Guided Future Hypotheses for Horizon-Aware Exploration in Multi-Step Robot Manipulation
FEC conditions policies on LLM-guided short-horizon future videos via a three-stage pipeline, yielding performance gains for BC+RL over no-future baselines on RoboCasa and CALVIN while mismatched futures degrade results.
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When Life Gives You BC, Make Q-functions: Extracting Q-values from Behavior Cloning for On-Robot Reinforcement Learning
Q2RL extracts Q-values from a BC policy and applies Q-gating to enable efficient offline-to-online RL, outperforming baselines on D4RL/robomimic tasks and achieving up to 100% success on real-robot manipulation in 1-2 hours.
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Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering
MoRE improves robot policy success rates by 44 percentage points by distilling mode redirection into weights, matching filtered retraining performance without inference overhead.
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Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems
A novel diffusion variant accelerates minimum-time planning for redundant dual-arm robots by replacing gradient-based solving of the nonconvex high-level problem with probabilistic sampling, yielding 35x faster runtime and 34% less path error.