# DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics

arXiv: https://arxiv.org/abs/2407.02274 · HTML: https://mlsanity.com/paper/2407.02274/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2024.

## Built on by (most cited first, 30 of 42)

- [DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping](https://mlsanity.com/paper/2502.20900/) (2025)
- [SplatSim: Zero-Shot Sim2Real Transfer of RGB Manipulation Policies Using Gaussian Splatting](https://mlsanity.com/paper/2409.10161/) (2024)
- [Visual Imitation Enables Contextual Humanoid Control](https://mlsanity.com/paper/2505.03729/) (2025)
- [Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids](https://mlsanity.com/paper/2502.20396/) (2025)
- [DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation](https://mlsanity.com/paper/2505.24853/) (2025)
- [Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration](https://mlsanity.com/paper/2504.12609/) (2025)
- [Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2506.17198/) (2025)
- [Local Policies Enable Zero-shot Long-horizon Manipulation](https://mlsanity.com/paper/2410.22332/) (2024)
- [AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency](https://mlsanity.com/paper/2502.16420/) (2025)
- [SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation](https://mlsanity.com/paper/2602.16863/) (2026)
- [Dex4D: Task-Agnostic Point Track Policy for Zero-Shot Dexterous Manipulation](https://mlsanity.com/paper/2602.15828/) (2026)
- [Computational-Statistical Tradeoffs at the Next-Token Prediction Barrier: Autoregressive and Imitation Learning under Misspecification](https://mlsanity.com/paper/2502.12465/) (2025)
- [Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration](https://mlsanity.com/paper/2509.09671/) (2025)
- [Scaffolding Dexterous Manipulation with Vision-Language Models](https://mlsanity.com/paper/2506.19212/) (2025)
- [Maestro: Orchestrating Robotics Modules with Vision-Language Models for Zero-Shot Generalist Robots](https://mlsanity.com/paper/2511.00917/) (2025)
- [Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning](https://mlsanity.com/paper/2505.12278/) (2025)
- [End-to-end RL Improves Dexterous Grasping Policies](https://mlsanity.com/paper/2509.16434/) (2025)
- [Human Universal Grasping](https://mlsanity.com/paper/2606.17054/) (2026)
- [LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations](https://mlsanity.com/paper/2508.17547/) (2025)
- [Flow with the Force Field: Learning 3D Compliant Flow Matching Policies from Force and Demonstration-Guided Simulation Data](https://mlsanity.com/paper/2510.02738/) (2025)
- [DeFM: Learning Foundation Representations from Depth for Robotics](https://mlsanity.com/paper/2601.18923/) (2026)
- [StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception](https://mlsanity.com/paper/2605.09989/) (2026)
- [RealMirror: A Comprehensive, Open-Source Vision-Language-Action Platform for Embodied AI](https://mlsanity.com/paper/2509.14687/) (2025)
- [OmniDexVLG: Learning Dexterous Grasp Generation from Vision Language Model-Guided Grasp Semantics, Taxonomy and Functional Affordance](https://mlsanity.com/paper/2512.03874/) (2025)
- [UniCross: Unified Cross-Skill Dexterous Manipulation Synthesis](https://mlsanity.com/paper/2607.28198/) (2026)
- [ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning](https://mlsanity.com/paper/2608.19182/) (2026)
- [Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control](https://mlsanity.com/paper/2509.06201/) (2025)
- [How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference](https://mlsanity.com/paper/2603.03280/) (2026)
- [Efficient On-policy Visual-RL via Stochastic Decoupled Policy Gradient](https://mlsanity.com/paper/2605.26478/) (2026)
- [Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?](https://mlsanity.com/paper/2606.26428/) (2026)

## Abstract (arXiv)

A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However, existing methods often have very limited speed, dexterity, and generality, along with limited or no hardware safety guarantees. In this work, we introduce DextrAH-G, a depth-based dexterous grasping policy trained entirely in simulation that combines reinforcement learning, geometric fabrics, and teacher-student distillation. We address key challenges in joint arm-hand policy learning, such as high-dimensional observation and action spaces, the sim2real gap, collision avoidance, and hardware constraints. DextrAH-G enables a 23 motor arm-hand robot to safely and continuously grasp and transport a large variety of objects at high speed using multi-modal inputs including depth images, allowing generalization across object geometry. Videos at https://sites.google.com/view/dextrah-g.