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DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics
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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.
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Cited by 9 Pith papers
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Visual Imitation Enables Contextual Humanoid Control
A single policy trained from 123 monocular videos, fine-tuned in simulation, and distilled to heightmap plus root-direction inputs lets a Unitree G1 climb stairs and sit and stand on real furniture.
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A dexterous robot hand learns to grasp novel objects from color images alone, trained purely in simulation, and demonstrates competitive real-world performance versus depth-camera policies.
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Grasp to Act: Dexterous Grasping for Tool Use in Dynamic Settings
Combining wrench-tested grasp optimization with real-time RL finger adjustments lets a 16-DoF robot hand keep tools stable during hammering, sawing, cutting, stirring, and scooping.
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Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration
Dexplore learns dexterous robotic hand control from human MoCap demonstrations by treating them as soft, adaptively shrinking spatial references, then distills the policy into a vision-based controller.
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LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
LodeStar combines automatic skill segmentation with simulation-based reinforcement learning augmentation and a learned routing transformer to let a robotic hand complete long-horizon dexterous tasks from a few human demos.
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DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
DexMachina uses decaying virtual object controllers as a curriculum to train bimanual dexterous policies that track demonstrated object states, and reports large gains over baselines on a new six-hand benchmark.
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Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning
PDC trains a single egocentric-vision policy that lets a simulated humanoid search for, grasp, and place objects and open drawers without privileged state information.
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Grasp What You Want: Embodied Dexterous Grasping System Driven by Your Voice
EDGS integrates vision-language enrichment with analytical dexterous grasp planning, reporting high success rates for voice-commanded grasping in cluttered real-world scenes.
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.
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