EgoEngine transforms egocentric human videos into high-fidelity robot data enabling zero-shot visuomotor dexterous policy learning without real-robot demonstrations.
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8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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DEHP adds an online-RL horizon predictor to frozen chunk policies, yielding higher success on precise and long-horizon robot manipulation by adapting chunk length to task stage.
EXPO stabilizes online RL for expressive policies by training a base policy with imitation and using a lightweight Gaussian edit policy to select higher-value actions on the fly for sampling and TD backups.
DSRL steers pretrained diffusion policies for robotics by applying RL to their latent noise inputs, achieving sample-efficient real-world adaptation with only black-box access.
RoHIL adapts human-in-the-loop RL policies to new illumination conditions offline by combining world-model image relighting, illumination-retention replay, and anchored Bellman regularisation, improving shifted-light performance while preserving source performance on four real-robot tasks.
DPPO fine-tunes diffusion policies via policy gradients and outperforms prior RL approaches for diffusion policies and PG-tuned alternatives on robot benchmarks while enabling stable training and hardware deployment.
HDFlow pairs a high-level diffusion planner for strategic subgoals with a low-level rectified flow planner for efficient trajectories, claiming superior performance on furniture assembly and other long-horizon robotic benchmarks.
Human-video dynamics models enable cross-embodiment robot self-improvement via training-free Dynamics-Guided Action Correction, raising success rates from 40% to 81% on seven real-world tasks.
citing papers explorer
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EgoEngine: From Egocentric Human Videos to High-Fidelity Dexterous Robot Demonstrations
EgoEngine transforms egocentric human videos into high-fidelity robot data enabling zero-shot visuomotor dexterous policy learning without real-robot demonstrations.
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Dynamic Execution Horizon Prediction for Chunk-based Robot Policies
DEHP adds an online-RL horizon predictor to frozen chunk policies, yielding higher success on precise and long-horizon robot manipulation by adapting chunk length to task stage.
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Steering Your Diffusion Policy with Latent Space Reinforcement Learning
DSRL steers pretrained diffusion policies for robotics by applying RL to their latent noise inputs, achieving sample-efficient real-world adaptation with only black-box access.
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RoHIL: Robust Human-in-the-Loop Robotic Reinforcement Learning Against Illumination Variations
RoHIL adapts human-in-the-loop RL policies to new illumination conditions offline by combining world-model image relighting, illumination-retention replay, and anchored Bellman regularisation, improving shifted-light performance while preserving source performance on four real-robot tasks.
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Diffusion Policy Policy Optimization
DPPO fine-tunes diffusion policies via policy gradients and outperforms prior RL approaches for diffusion policies and PG-tuned alternatives on robot benchmarks while enabling stable training and hardware deployment.
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HDFlow: Hierarchical Diffusion-Flow Planning for Long-horizon Tasks
HDFlow pairs a high-level diffusion planner for strategic subgoals with a low-level rectified flow planner for efficient trajectories, claiming superior performance on furniture assembly and other long-horizon robotic benchmarks.
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Robot Self-Improvement via Human-Video Dynamics Models
Human-video dynamics models enable cross-embodiment robot self-improvement via training-free Dynamics-Guided Action Correction, raising success rates from 40% to 81% on seven real-world tasks.