Learning Dexterous Grasping from Sparse Taxonomy Guidance
Dexterous manipulation requires planning a grasp configuration suited to the object and task, which is then executed through coordinated multi-finger control. However, specifying grasp plans with dense pose or contact targets for every object and task is impractical. Meanwhile, end-to-end reinforcement learning from task rewards alone lacks controllability, making it difficult for users to intervene when failures occur. To this end, we present GRIT, a two-stage framework that learns dexterous control from sparse taxonomy guidance. GRIT first predicts a taxonomy-based grasp specification from the scene and task context. Conditioned on this sparse command, a policy generates continuous fing...
| Authors | Juhan Park, Taerim Yoon, Seungmin Kim et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 April 2026 |
| Last updated at source | 30 June 2026 |
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