# DexTransfer: Real World Multi-fingered Dexterous Grasping with Minimal Human Demonstrations

arXiv: https://arxiv.org/abs/2209.14284 · HTML: https://mlsanity.com/paper/2209.14284/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 30 of 31)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Solving Rubik's Cube with a Robot Hand](https://mlsanity.com/paper/1910.07113/) (2019)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Learning Dexterous In-Hand Manipulation](https://mlsanity.com/paper/1808.00177/) (2018)
- [GRAB: A Dataset of Whole-Body Human Grasping of Objects](https://mlsanity.com/paper/2008.11200/) (2020)
- [Learning joint reconstruction of hands and manipulated objects](https://mlsanity.com/paper/1904.05767/) (2019)
- [The CMA Evolution Strategy: A Tutorial](https://mlsanity.com/paper/1604.00772/) (2016)
- [DexYCB: A Benchmark for Capturing Hand Grasping of Objects](https://mlsanity.com/paper/2104.04631/) (2021)
- [Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics](https://mlsanity.com/paper/1703.09312/) (2017)
- [6-DOF GraspNet: Variational Grasp Generation for Object Manipulation](https://mlsanity.com/paper/1905.10520/) (2019)
- [First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations](https://mlsanity.com/paper/1704.02463/) (2017)
- [Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes](https://mlsanity.com/paper/2103.14127/) (2021)
- [Deep Dynamics Models for Learning Dexterous Manipulation](https://mlsanity.com/paper/1909.11652/) (2019)
- [DexMV: Imitation Learning for Dexterous Manipulation from Human Videos](https://mlsanity.com/paper/2108.05877/) (2021)
- [Grasping Field: Learning Implicit Representations for Human Grasps](https://mlsanity.com/paper/2008.04451/) (2020)
- [A System for General In-Hand Object Re-Orientation](https://mlsanity.com/paper/2111.03043/) (2021)
- [DexPilot: Vision Based Teleoperation of Dexterous Robotic Hand-Arm System](https://mlsanity.com/paper/1910.03135/) (2019)
- [D-Grasp: Physically Plausible Dynamic Grasp Synthesis for Hand-Object Interactions](https://mlsanity.com/paper/2112.03028/) (2021)
- [Visual Imitation Made Easy](https://mlsanity.com/paper/2008.04899/) (2020)
- [Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations](https://mlsanity.com/paper/1912.04344/) (2019)
- [State-Only Imitation Learning for Dexterous Manipulation](https://mlsanity.com/paper/2004.04650/) (2020)
- [DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video](https://mlsanity.com/paper/2202.00164/) (2022)
- [ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact](https://mlsanity.com/paper/1904.03754/) (2019)
- [Learning Dexterous Grasping with Object-Centric Visual Affordances](https://mlsanity.com/paper/2009.01439/) (2020)
- [Dexterous Manipulation with Deep Reinforcement Learning: Efficient, General, and Low-Cost](https://mlsanity.com/paper/1810.06045/) (2018)
- [Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation](https://mlsanity.com/paper/2007.15157/) (2020)
- [Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning](https://mlsanity.com/paper/2008.03285/) (2020)
- [Learning Dexterous Manipulation for a Soft Robotic Hand from Human Demonstration](https://mlsanity.com/paper/1603.06348/) (2016)
- [Learning Task-Oriented Grasping from Human Activity Datasets](https://mlsanity.com/paper/1910.11669/) (2019)
- [Learning Dexterous Manipulation from Suboptimal Experts](https://mlsanity.com/paper/2010.08587/) (2020)

## Built on by (most cited first, 22 of 22)

- [DexCap: Scalable and Portable Mocap Data Collection System for Dexterous Manipulation](https://mlsanity.com/paper/2403.07788/) (2024)
- [DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping](https://mlsanity.com/paper/2502.20900/) (2025)
- [Scalable Vision-Language-Action Model Pretraining for Robotic Manipulation with Real-Life Human Activity Videos](https://mlsanity.com/paper/2510.21571/) (2025)
- [DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects](https://mlsanity.com/paper/2305.05706/) (2023)
- [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)
- [Hand-Object Interaction Pretraining from Videos](https://mlsanity.com/paper/2409.08273/) (2024)
- [Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2506.17198/) (2025)
- [GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping](https://mlsanity.com/paper/2309.06038/) (2023)
- [DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation](https://mlsanity.com/paper/2411.18562/) (2024)
- [Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration](https://mlsanity.com/paper/2509.09671/) (2025)
- [DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos](https://mlsanity.com/paper/2510.08475/) (2025)
- [AoE: Always-on Egocentric Human Video Collection for Embodied AI](https://mlsanity.com/paper/2602.23893/) (2026)
- [Traj2Action: A Co-Denoising Framework for Trajectory-Guided Human-to-Robot Skill Transfer](https://mlsanity.com/paper/2510.00491/) (2025)
- [Dexterous Grasping with Real-World Robotic Reinforcement Learning](https://mlsanity.com/paper/2503.04014/) (2025)
- [Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos](https://mlsanity.com/paper/2602.13197/) (2026)
- [AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection](https://mlsanity.com/paper/2606.23689/) (2026)
- [FunGrasp: Functional Grasping for Diverse Dexterous Hands](https://mlsanity.com/paper/2411.16755/) (2024)
- [D3Grasp: Diverse and Deformable Dexterous Grasping for General Objects](https://mlsanity.com/paper/2509.19892/) (2025)
- [Learning Dexterous Grasping from Sparse Taxonomy Guidance](https://mlsanity.com/paper/2604.04138/) (2026)
- [Hand2Robot: Object-Centric 4D Hand Reconstruction from Human Demonstrations for Robotics](https://mlsanity.com/paper/2610.15769/) (2026)

## Abstract (arXiv)

Teaching a multi-fingered dexterous robot to grasp objects in the real world has been a challenging problem due to its high dimensional state and action space. We propose a robot-learning system that can take a small number of human demonstrations and learn to grasp unseen object poses given partially occluded observations. Our system leverages a small motion capture dataset and generates a large dataset with diverse and successful trajectories for a multi-fingered robot gripper. By adding domain randomization, we show that our dataset provides robust grasping trajectories that can be transferred to a policy learner. We train a dexterous grasping policy that takes the point clouds of the object as input and predicts continuous actions to grasp objects from different initial robot states. We evaluate the effectiveness of our system on a 22-DoF floating Allegro Hand in simulation and a 23-DoF Allegro robot hand with a KUKA arm in real world. The policy learned from our dataset can generalize well on unseen object poses in both simulation and the real world