# DexRepNet: Learning Dexterous Robotic Grasping Network with Geometric and Spatial Hand-Object Representations

arXiv: https://arxiv.org/abs/2303.09806 · HTML: https://mlsanity.com/paper/2303.09806/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2023.

## Built on by (most cited first, 16 of 16)

- [TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding](https://mlsanity.com/paper/2401.08399/) (2024)
- [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://mlsanity.com/paper/2503.21860/) (2025)
- [GraspXL: Generating Grasping Motions for Diverse Objects at Scale](https://mlsanity.com/paper/2403.19649/) (2024)
- [ArtiGrasp: Physically Plausible Synthesis of Bi-Manual Dexterous Grasping and Articulation](https://mlsanity.com/paper/2309.03891/) (2023)
- [ViViDex: Learning Vision-based Dexterous Manipulation from Human Videos](https://mlsanity.com/paper/2404.15709/) (2024)
- [AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency](https://mlsanity.com/paper/2502.16420/) (2025)
- [Learning Diverse Bimanual Dexterous Manipulation Skills from Human Demonstrations](https://mlsanity.com/paper/2410.02477/) (2024)
- [SyncDiff: Synchronized Motion Diffusion for Multi-Body Human-Object Interaction Synthesis](https://mlsanity.com/paper/2412.20104/) (2024)
- [ContactDexNet: Multi-fingered Robotic Hand Grasping in Cluttered Environments through Hand-object Contact Semantic Mapping](https://mlsanity.com/paper/2404.08844/) (2024)
- [VTAO-BiManip: Masked Visual-Tactile-Action Pre-training with Object Understanding for Bimanual Dexterous Manipulation](https://mlsanity.com/paper/2501.03606/) (2025)
- [MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping](https://mlsanity.com/paper/2510.06068/) (2025)
- [Hierarchical Reinforcement Learning for Articulated Tool Manipulation with Multifingered Hand](https://mlsanity.com/paper/2507.06822/) (2025)
- [Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers](https://mlsanity.com/paper/2507.11287/) (2025)
- [Scalable Dexterous Robot Learning with AR-based Remote Human-Robot Interactions](https://mlsanity.com/paper/2602.07341/) (2026)
- [DragMesh-2: Physically Plausible Dexterous Hand-Object Interaction with Articulated Objects](https://mlsanity.com/paper/2606.15133/) (2026)
- [2nd Place Solution to the HANDS 2026 Workshop Challenge-Dexterous Grasp Motion Track: Single-Shot Trajectory Warping for Grasp Motion Generation](https://mlsanity.com/paper/2609.21511/) (2026)

## Abstract (arXiv)

Robotic dexterous grasping is a challenging problem due to the high degree of freedom (DoF) and complex contacts of multi-fingered robotic hands. Existing deep reinforcement learning (DRL) based methods leverage human demonstrations to reduce sample complexity due to the high dimensional action space with dexterous grasping. However, less attention has been paid to hand-object interaction representations for high-level generalization. In this paper, we propose a novel geometric and spatial hand-object interaction representation, named DexRep, to capture dynamic object shape features and the spatial relations between hands and objects during grasping. DexRep comprises Occupancy Feature for rough shapes within sensing range by moving hands, Surface Feature for changing hand-object surface distances, and Local-Geo Feature for local geometric surface features most related to potential contacts. Based on the new representation, we propose a dexterous deep reinforcement learning method to learn a generalizable grasping policy DexRepNet. Experimental results show that our method outperforms baselines using existing representations for robotic grasping dramatically both in grasp success rate and convergence speed. It achieves a 93% grasping success rate on seen objects and higher than 80% grasping success rates on diverse objects of unseen categories in both simulation and real-world experiments.