T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at
Overview
This research introduces T(R,O) Grasp++, a scalable diffusion-based framework for cross-embodiment dexter, grasping in cluttered environments. The model achieves an average success rate of 88% across eight dexterous embodiments, with 0.3s inference latency and improved sample efficiency.
From arXiv Robotics — research abstracts
Event evidence and corrections
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Reported quantity · dexterous hands: 8 other · Basis not reported
“arXiv:2610.25410v1 Announce Type: new Abstract: Dexterous grasping in cluttered scenes remains a central challenge in robotic manipulation, as it requires high-dimensional hand coordination under complex object-environment constraints. To address this problem, we introduce one of the largest simulation benchmarks to date, comprising around 0.5B grasp annotations across eight dexterous hands and approximately 50K obje”
Exact source · revision 1Source owner not reported
Reported quantity · objects: 50000 other · Basis not reported
“arXiv:2610.25410v1 Announce Type: new Abstract: Dexterous grasping in cluttered scenes remains a central challenge in robotic manipulation, as it requires high-dimensional hand coordination under complex object-environment constraints. To address this problem, we introduce one of the largest simulation benchmarks to date, comprising around 0.5B grasp annotations across eight dexterous hands and approximately 50K obje”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 88 percent · Basis not reported
“cts. Furthermore, we propose T(R,O) Grasp++, a scalable diffusion-based framework for cross-embodiment dexterous grasp synthesis in cluttered environments. Extensive experiments demonstrate that our model significantly outperforms existing baselines, achieving an average success rate of 88% across eight dexterous embodiments. Meanwhile, our pipeline achieves only 0.3s inference latency while improving inference sampl”
Exact source · revision 1Source owner not reported
Reported quantity · inference latency: 0.3 other · Basis not reported
“cts. Furthermore, we propose T(R,O) Grasp++, a scalable diffusion-based framework for cross-embodiment dexterous grasp synthesis in cluttered environments. Extensive experiments demonstrate that our model significantly outperforms existing baselines, achieving an average success rate of 88% across eight dexterous embodiments. Meanwhile, our pipeline achieves only 0.3s inference latency while improving inference sampl”
Exact source · revision 1Source owner not reported
Evidence dependency changes (2)
- 2026-10-30T22:18:58.308Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:18:53.893Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalT(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale
This research introduces T(R,O) Grasp++, a scalable diffusion-based framework for cross-embodiment dexter, grasping in cluttered environments. The model achieves an average success rate of 88% across eight dexterous embodiments, with 0.3s inference latency and improved sample efficiency.
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