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ARXIV:2603.05312 · ROBOTICS · SUBMITTED 19 MAR · 18:48 UTC · FRESHNESS STALE
ARXIV:2603.05312ROBOTICSSUBMITTED 19 MAR · 18:48 UTCFRESHNESS STALEarXiv
A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer.
Opportunity summary
Pain A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer.
Evidence 0 refs | 0 sources | 33% coverage
Blocker Evidence unverified
A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape,…
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight of objects, enabling robust…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Trained exclusively on synthetic data, the policy achieves robust zero-shot sim-to-real transfer and consistently succeeds on novel objects with varied shapes, sizes, and weights,…
Robotics moved forward this cycle; last verified April 2026. Public score 8.0/10.
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A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer.
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10.48550/arXiv.2603.05312A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer.
Abstract
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight of objects, enabling robust grasping and subsequent manipulation. In contrast, current robotic grasping remains limited, particularly in multi-strategy settings. Although substantial efforts have targeted parallel-gripper and single-hand grasping, dexterous grasping for bimanual robots remains underexplored, with data being a primary bottleneck. Achieving physically plausible and geometrically conforming grasps that can withstand external wrenches poses significant challenges. To address these issues, we introduce UltraDexGrasp, a framework for universal dexterous grasping with bimanual robots. The proposed data-generation pipeline integrates optimization-based grasp synthesis with planning-based demonstration generation, yielding high-quality and diverse trajectories across multiple grasp strategies. With this framework, we curate UltraDexGrasp-20M, a large-scale, multi-strategy grasp dataset comprising 20 million frames across 1,000 objects. Based on UltraDexGrasp-20M, we further develop a simple yet effective grasp policy that takes point clouds as input, aggregates scene features via unidirectional attention, and predicts control commands. Trained exclusively on synthetic data, the policy achieves robust zero-shot sim-to-real transfer and consistently succeeds on novel objects with varied shapes, sizes, and weights, attaining an average success rate of 81.2% in real-world universal dexterous grasping. To facilitate future research on grasping with bimanual robots, we open-source the data generation pipeline at https://github.com/InternRobotics/UltraDexGrasp.
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Viability
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Dimensions overall score 8.0
PROBLEM
A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight o...
METHOD
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight of objects, enabling robust grasping and subsequent manipulation.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Trained exclusively on synthetic data, the policy achieves robust zero-shot sim-to-real transfer and consistently succeeds on novel objects with varied shapes, sizes, and weights, attaining an average suc...
WHY NOW
Robotics moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight of objects, enabling robust grasping and subsequent manipulation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Grasping is a fundamental capability for robots to interact with the physical world. Humans, equipped with two hands, autonomously select appropriate grasp strategies based on the shape, size, and weight of objects, enabling robust grasping and subsequent manipulation.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Trained exclusively on synthetic data, the policy achieves robust zero-shot sim-to-real transfer and consistently succeeds on novel objects with varied shapes, sizes, and weights, attaining an average success rate of 81.2% in real-world universal dexterous grasping.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Robotics moved forward this cycle; last verified April 2026. Public score 8.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for universal dexterous grasping with bimanual robots, trained on a large-scale synthetic dataset, enabling robust zero-shot sim-to-real transfer.
Segment
Robotics
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