Dexora is the first open-source VLA system for dual-arm dual-hand high-DoF manipulation, trained on 100K simulated and 10K real teleoperated trajectories with a discriminator-weighted diffusion policy, achieving 66.7% dexterous success versus 51.7% for baselines.
Dexonomy: Synthesiz- ing all dexterous grasp types in a grasp taxonomy
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Generalizable dexterous grasping with suitable grasp types is a fundamental skill for intelligent robots. Developing such skills requires a large-scale and high-quality dataset that covers numerous grasp types (i.e., at least those categorized by the GRASP taxonomy), but collecting such data is extremely challenging. Existing automatic grasp synthesis methods are often limited to specific grasp types or object categories, hindering scalability. This work proposes an efficient pipeline capable of synthesizing contact-rich, penetration-free, and physically plausible grasps for any grasp type, object, and articulated hand. Starting from a single human-annotated template for each hand and grasp type, our pipeline tackles the complicated synthesis problem with two stages: optimize the object to fit the hand template first, and then locally refine the hand to fit the object in simulation. To validate the synthesized grasps, we introduce a contact-aware control strategy that allows the hand to apply the appropriate force at each contact point to the object. Those validated grasps can also be used as new grasp templates to facilitate future synthesis. Experiments show that our method significantly outperforms previous type-unaware grasp synthesis baselines in simulation. Using our algorithm, we construct a dataset containing 10.7k objects and 9.5M grasps, covering 31 grasp types in the GRASP taxonomy. Finally, we train a type-conditional generative model that successfully performs the desired grasp type from single-view object point clouds, achieving an 82.3% success rate in real-world experiments. Project page: https://pku-epic.github.io/Dexonomy.
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Wrist-guided whole-body RL retargets human–object interactions without finger pose supervision, matching supervised methods and generalizing across hand morphologies in simulation.
KPGrasp is a scalable Transformer flow-matching model using 3D hand keypoints that achieves 76.3% success on Dexonomy (47.4% improvement) and best average on DexGrasp Anything without contact losses or test-time refinement.
A multi-agent RL high-level planner outputs task-space velocities that a GPU-parallel QP low-level controller converts to joint velocities while enforcing limits and collisions, yielding robust sim-to-real dexterous grasping with zero-shot steerability.
Dense spatial modulation of multiple 3D LUTs plus an uncertainty-weighted reconstruction loss improves mixed under/over-exposure correction over global-modulation baselines on four benchmarks.
DextER uses contact-based embodied reasoning via autoregressive token generation to produce language-driven dexterous grasps, reaching 67.14% success on DexGYS with a 3.83 p.p. gain over prior methods and 96.4% better intention alignment.
ZeroDex grounds VLM outputs into 3D keypoints via multi-view triangulation and ray voting to enable zero-shot long-horizon dexterous manipulation with closed-loop replanning.
citing papers explorer
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Dexora: Open-source VLA for High-DoF Bimanual Dexterity
Dexora is the first open-source VLA system for dual-arm dual-hand high-DoF manipulation, trained on 100K simulated and 10K real teleoperated trajectories with a discriminator-weighted diffusion policy, achieving 66.7% dexterous success versus 51.7% for baselines.
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WristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation
Wrist-guided whole-body RL retargets human–object interactions without finger pose supervision, matching supervised methods and generalizing across hand morphologies in simulation.
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KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation
KPGrasp is a scalable Transformer flow-matching model using 3D hand keypoints that achieves 76.3% success on Dexonomy (47.4% improvement) and best average on DexGrasp Anything without contact losses or test-time refinement.
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Learning Reactive Dexterous Grasping via Hierarchical Task-Space RL Planning and Joint-Space QP Control
A multi-agent RL high-level planner outputs task-space velocities that a GPU-parallel QP low-level controller converts to joint velocities while enforcing limits and collisions, yielding robust sim-to-real dexterous grasping with zero-shot steerability.
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Learning Dexterous Grasping from Sparse Taxonomy Guidance
Dense spatial modulation of multiple 3D LUTs plus an uncertainty-weighted reconstruction loss improves mixed under/over-exposure correction over global-modulation baselines on four benchmarks.
-
DextER: Language-driven Dexterous Grasp Generation with Embodied Reasoning
DextER uses contact-based embodied reasoning via autoregressive token generation to produce language-driven dexterous grasps, reaching 67.14% success on DexGYS with a 3.83 p.p. gain over prior methods and 96.4% better intention alignment.
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ZeroDex: Zero-Shot Long-Horizon Dexterous Manipulation via Multi-View 3D-Grounded VLM Reasoning
ZeroDex grounds VLM outputs into 3D keypoints via multi-view triangulation and ray voting to enable zero-shot long-horizon dexterous manipulation with closed-loop replanning.