GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation
We present GR-RL, a robotic learning framework that turns a generalist vision-language-action (VLA) policy into a highly capable specialist for long-horizon dexterous manipulation. Assuming the optimality of human demonstrations is core to existing VLA policies. However, we claim that in highly dexterous and precise manipulation tasks, human demonstrations are noisy and suboptimal. GR-RL proposes a multi-stage training pipeline that filters, augments, and reinforces the demonstrations by reinforcement learning. First, GR-RL learns a vision-language-conditioned task progress, filters the demonstration trajectories, and only keeps the transitions that contribute positively to the progress. Specifically, we show that by directly applying offline RL with sparse reward, the resulting Q-values can be treated as a robust progress function. Next, we introduce morphological symmetry augmentation that greatly improves the generalization and performance of GR-RL. Lastly, to better align the VLA policy with its deployment behaviors for high-precision control, we perform online RL by learning a latent space noise predictor. With this pipeline, GR-RL is, to our knowledge, the first learning-based policy that can autonomously lace up a shoe by threading shoelaces through multiple eyelets with an 83.3% success rate, a task requiring long-horizon reasoning, millimeter-level precision, and compliant soft-body interaction. We hope GR-RL provides a step toward enabling generalist robot foundations models to specialize into reliable real-world experts.
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Yunfei Li
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Xiao Ma
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Jiafeng Xu
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Yu Cui
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Zhongren Cui
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Zhigang Han
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Liqun Huang
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Tao Kong
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2025-12-25T20:53:44.372Z
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Kong
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Yuxiao Liu
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2026-03-03T10:45:17.736Z
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Yuxiao Liu
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Hao Niu
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Wanli Peng
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Jingchao Qiao
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Zeyu Ren
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Haixin Shi
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Zhi Su
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2025-12-02T08:40:45.705Z
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苏智
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Jiawen Tian
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Yuyang Xiao
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Shenyu Zhang
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Liwei Zheng
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Hang Li
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Yonghui Wu
GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation
We present GR-RL, a robotic learning framework that turns a generalist vision-language-action (VLA) policy into a highly capable specialist for long-horizon dexterous manipulation. Assuming the optimality of human demonstrations is core to existing VLA policies. However, we claim that in highly dexterous and precise manipulation tasks, human demonstrations are noisy and suboptimal. GR-RL proposes a multi-stage training pipeline that filters, augments, and reinforces the demonstrations by reinforcement learning. First, GR-RL learns a vision-language-conditioned task progress, filters the demonstration trajectories, and only keeps the transitions that contribute positively to the progress. Specifically, we show that by directly applying offline RL with sparse reward, the resulting Q-values can be treated as a robust progress function. Next, we introduce morphological symmetry augmentation that greatly improves the generalization and performance of GR-RL. Lastly, to better align the VLA policy with its deployment behaviors for high-precision control, we perform online RL by learning a latent space noise predictor. With this pipeline, GR-RL is, to our knowledge, the first learning-based policy that can autonomously lace up a shoe by threading shoelaces through multiple eyelets with an 83.3% success rate, a task requiring long-horizon reasoning, millimeter-level precision, and compliant soft-body interaction. We hope GR-RL provides a step toward enabling generalist robot foundations models to specialize into reliable real-world experts.
GR-RL enhances a vision-language-action policy for long-horizon dexterous manipulation through a multi-stage training pipeline that filters, augments, and refines demonstrations using reinforcement learning.
vision-language-action (VLA) policy reinforcement learning task progress offline RL sparse reward $Q$-values morphological symmetry augmentation online RL latent space noise predictor dexterous manipulation long-horizon reasoning millimeter-level precision compliant soft-body interaction