Data to 14 Oct 2026

DextrAH-RGB: Visuomotor Policies to Grasp Anything with Dexterous Hands

arXiv 2412.01791 ↗First posted 27 Nov 2024cs.ROcited by 45

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Abstract (arXiv)

One of the most important, yet challenging, skills for a dexterous robot is grasping a diverse range of objects. Much of the prior work has been limited by speed, generality, or reliance on depth maps and object poses. In this paper, we introduce DextrAH-RGB, a system that can perform dexterous arm-hand grasping end-to-end from RGB image input. We train a privileged fabric-guided policy (FGP) in simulation through reinforcement learning that acts on a geometric fabric controller to dexterously grasp a wide variety of objects. We then distill this privileged FGP into a RGB-based FGP strictly in simulation using photorealistic tiled rendering. To our knowledge, this is the first work that is able to demonstrate robust sim2real transfer of an end2end RGB-based policy for complex, dynamic, contact-rich tasks such as dexterous grasping. DextrAH-RGB is competitive with depth-based dexterous grasping policies, and generalizes to novel objects with unseen geometry, texture, and lighting conditions in the real world. Videos of our system grasping a diverse range of unseen objects are available at urlhttps://dextrah-rgb.github.io/.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Nov 2024.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 20; 2026: 25.

45 papers.

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2025DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Graspingcited by 89
2025Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoidscited by 48
2025DexWild: Dexterous Human Interactions for In-the-Wild Robot Policiescited by 38
2025Dex1B: Learning with 1B Demonstrations for Dexterous Manipulationcited by 24
2025The Reality Gap in Robotics: Challenges, Solutions, and Best Practicescited by 24
2025StereoVLA: Enhancing Vision-Language-Action Models with Stereo Visioncited by 18
2025AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiencycited by 16
2025Learning Sim-to-Real Humanoid Locomotion in 15 Minutescited by 16
2025Opening the Sim-to-Real Door for Humanoid Pixel-to-Action Policy Transfercited by 15
2026SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulationcited by 15
2026Dex4D: Task-Agnostic Point Track Policy for Zero-Shot Dexterous Manipulationcited by 14
2025Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Explorationcited by 11

33 more

2026Emergent Dexterity via Diverse Resets and Large-Scale Reinforcement Learningcited by 10
2025Scaffolding Dexterous Manipulation with Vision-Language Modelscited by 9
2025Learning Dexterous Manipulation Skills from Imperfect Simulationscited by 9
2026Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptationcited by 6
2025Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learningcited by 5
2025SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-trainingcited by 5
2025End-to-end RL Improves Dexterous Grasping Policiescited by 5
2026LUCID: Learning Embodiment-Agnostic Intent Models from Unstructured Human Videos for Scalable Dexterous Robot Skill Acquisitioncited by 5
2026Human Universal Graspingcited by 5
2025Staggered Environment Resets Improve Massively Parallel On-Policy Reinforcement Learningcited by 3
2026DeFM: Learning Foundation Representations from Depth for Roboticscited by 3
2026StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perceptioncited by 3
2025From Power to Precision: Learning Fine-grained Dexterity for Multi-fingered Robotic Handscited by 2
2026Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worldscited by 2
2026ViserDex: Visual Sim-to-Real for Robust Dexterous In-hand Reorientationcited by 2
2026ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learningcited by 2
2025Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Controlcited by 1
2026ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priorscited by 1
2026Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?cited by 1
2026DexAgent: An Agentic Human2Sim2Robot Framework for Dexterous Manipulation with Self-Evolving Tool Librarycited by 1
2025Multi-Group Equivariant Augmentation for Reinforcement Learning in Robot Manipulationcited by 0
2025D3Grasp: Diverse and Deformable Dexterous Grasping for General Objectscited by 0
2026Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulationcited by 0
2026Bridging the Gap: Enabling Soft Actor Critic for High Performance Legged Locomotioncited by 0
2026Mana: Dexterous Manipulation of Articulated Toolscited by 0
2026Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulationcited by 0
2026SILO: Simulation-in-the-Loop Sim-to-Real Transfer for Multi-Stage Cable Routingcited by 0
2026Pre-training Visual Dexterity in Simulationcited by 0
2026WeaveRL: Weaving Reconstruction into Scene-Aware Fabrics for Perceptive Reinforcement Learningcited by 0
2026An Empirical Study on What Matters for Viewpoint-Generalizable Policies in Visual Imitation Learningcited by 0
2026Estimate, Don't Imitate: Reusing Differentiable State-Based Policies for Visuomotor Controlcited by 0
2026A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Controlcited by 0
2026AlphaNIST: Emergent Assembly Skills from Binary Task Rewardscited by 0

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