October 3, 2025Open Access

Dexterous Grasping with Real-World Robotic Reinforcement Learning

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DexGraspRL achieves a nearly 92% success rate in diverse dexterous grasping tasks, showcasing its effectiveness.
The method balances reinforcement learning and preservation of the pretrained policy to manage the catastrophic forgetting phenomenon.
Imitation learning is used to pretrain the policy, enhancing the robot’s grasping abilities with expert demonstrations.
Fine-tuning through reinforcement learning leads to a 23% reduction in average cycle time, indicating improved efficiency.

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Proposed DexGraspRL improves dexterous grasping in real-world environments with policies from reinforcement learning, indicating better adaptability.

Huang et al. (2025) studied this question.

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Proposed DexGraspRL improves dexterous grasping in real-world environments with policies from reinforcement learning, indicating better adaptability.