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2025

Dexterous Grasping with Real-World Robotic Reinforcement Learning

Huang, Dongchi, Zhang, Tianle, Li, Yihang et al.

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Dexterous grasping in the real world presents a fundamental and significant challenge for robot learning.

The ability to employ affordance-aware poses to grasp objects with diverse geometries and properties in arbitrary scenarios is essential for general-purpose robots.
However, existing research predominantly addresses dexterous grasping problems within simulators, which encounter difficulties when applied in real-world environments due to the domain gap between reality and simulation.
This limitation hinders their generalizability and practicality in real-world applications.

Dexterous Grasping with Real-World Robotic Reinforcement Learning · Around

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Y. Liu, Y. Yang, Y. Wang, X. Wu, J. Wang, Y. Yao, S. Schwertfeger, S. Yang, W. Wang, J. Yu et al. , “Realdex: Towards human-like grasping for robotic dexterous hand,” arXiv preprint arXiv:2402.13853 , 2024
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