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Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning
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Reinforcement learning (RL) holds great promise for enabling autonomous acquisition of complex robotic manipulation skills, but realizing this potential in real-world settings has been challenging. We present a human-in-the-loop vision-based RL system that demonstrates impressive performance on a diverse set of dexterous manipulation tasks, including dynamic manipulation, precision assembly, and dual-arm coordination. Our approach integrates demonstrations and human corrections, efficient RL algorithms, and other system-level design choices to learn policies that achieve near-perfect success rates and fast cycle times within just 1 to 2.5 hours of training. We show that our method significantly outperforms imitation learning baselines and prior RL approaches, with an average 2x improvement in success rate and 1.8x faster execution. Through extensive experiments and analysis, we provide insights into the effectiveness of our approach, demonstrating how it learns robust, adaptive policies for both reactive and predictive control strategies. Our results suggest that RL can indeed learn a wide range of complex vision-based manipulation policies directly in the real world within practical training times. We hope this work will inspire a new generation of learned robotic manipulation techniques, benefiting both industrial applications and research advancements. Videos and code are available at our project website https://hil-serl.github.io/.
Forward citations
Cited by 11 Pith papers
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For high-precision manipulation, required demonstration count grows as log(N) ∝ 1/(P−c), where the fitted c varies with sensors, expert, and task complexity.
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Human corrective actions can be inverted into noise-space targets that train a lightweight latent policy to steer frozen flow/diffusion robot models from a handful of interventions while preserving pretrained skills.
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.
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RhinoVLA Technical Report
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$M^2$-VLA: Boosting Vision-Language Models for Generalizable Manipulation via Layer Mixture and Meta-Skills
M²-VLA shows that generalized VLMs can serve as direct backbones for robotic manipulation by selectively extracting task-critical features via Mixture of Layers and adding Meta Skill Modules for efficient trajectory learning.
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Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials
Prior Reinforce adapts a few demonstration motions to new goals in dynamic manipulation by learning a diffusion motion prior and refining a low-dimensional condition via Bayesian optimization, reaching new goals in un...
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TeViR: Text-to-Video Reward with Diffusion Models for Efficient Reinforcement Learning
TeViR uses a text-to-video diffusion model to generate future frames from a task description and rewards an RL agent for matching those frames, improving sample efficiency in robotic manipulation.
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Learning to Fold: prizewinning solution at LeHome Challenge 2026 (1st place online, 2nd offline)
A VLA policy with auxiliary success/progress heads and AWR+RECAP-style RL finished 1st in the LeHome 2026 simulation round and 2nd on the real robot.
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RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction
Robot policies trained on human interventions that rewind to a familiar state and then correct the mistake achieve higher long-horizon success and better data efficiency than imitation on full demonstrations alone.
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SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training
Simulation-pretrained policies, with digital-twin demos for critic bootstrapping and action proposals, cut real-world RL training time while reaching near-perfect success on three manipulation tasks.
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Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space
Breaking long manipulation tasks into atomic subtasks and collecting demonstrations from varied starting poses improves imitation learning success using fewer demonstration frames.
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