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About this paper
Zhipeng Tang, Sihang Chen, Sha Zhang, Peihao Yang, Yan Liu, Wentao Zhao, Xinrui Liu, Rui Huang and 6 more
LabDex introduces a large-scale, hierarchical benchmark and dataset for dexterous manipulation in chemistry laboratories, unifying real-world and simulation environments. This platform enables the training and evaluation of robotic systems across atomic, compositional, and long-horizon tasks, revealing bottlenecks in grasping, skill transition stability, and generalization for current robot learning methods.
Key insights
- Current robot learning methods, even advanced ones like \u03c00.5, face significant challenges in dexterous laboratory manipulation, with none completing full long-horizon tasks reliably.
- Key bottlenecks identified include reliable initial object grasping, maintaining stable object states during transitions between atomic skills, and robustly composing multiple skills.
- Generalization to novel objects is limited, with performance declining sharply for objects geometrically dissimilar to those in the training data, and performance being highly sensitive to visually similar distractors.
Summary by alphaXiv.
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