SimDuoReal: Object-Centric Bimanual Policy for Emergent Dexterity
Overview
This research introduces SimDuoReal, a reinforcement learning framework that achieves emergent bimanual dexterity through an object-centric reward objective. By training policies in varying environments rather than tuning task-specific rewards, the system discovers behaviors such as pre-grasp manipulation, grasping, regrasping, in-hand adjustment, and handover without manual motion sequences. The distilled single policy successfully completes 61 of 80 trials (76.3%) on real hardware across four tasks with unseen objects and no fine-tuning. In simulated barcode scanning, it achieves an 85.0% success rate compared to 60.4% for two single-arm policies without handover. The work suggests that specifying object-level goals provides a scalable interface for learning general bimanual manipulation, shifting the burden from manual coordination design to defining desired object interactions.
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Reported quantity · trials: 80 other · reported trials
“iverse bimanual tasks. On real hardware, the same policy succeeds in 61 of 80 trials (76.3\%) across four tasks with unseen objects and no task-specific fine-tuning. In simulated barcode scanning, our policy achieves 85.0\% success, compared with 60.4\% for two single-arm policies without handover. These results suggest that this objective provides a simple and scalable interface for learning general bimanual dexteri”
Exact source · revision 1Source owner not reported
Reported quantity · robots: 2 other · Basis not reported
“iverse bimanual tasks. On real hardware, the same policy succeeds in 61 of 80 trials (76.3\%) across four tasks with unseen objects and no task-specific fine-tuning. In simulated barcode scanning, our policy achieves 85.0\% success, compared with 60.4\% for two single-arm policies without handover. These results suggest that this objective provides a simple and scalable interface for learning general bimanual dexteri”
Exact source · revision 1Source owner not reported
Reported quantity · tasks: 4 other · Basis not reported
“iverse bimanual tasks. On real hardware, the same policy succeeds in 61 of 80 trials (76.3\%) across four tasks with unseen objects and no task-specific fine-tuning. In simulated barcode scanning, our policy achieves 85.0\% success, compared with 60.4\% for two single-arm policies without handover. These results suggest that this objective provides a simple and scalable interface for learning general bimanual dexteri”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSimDuoReal: Object-Centric Bimanual Policy for Emergent Dexterity
This research introduces SimDuoReal, a reinforcement learning framework that achieves emergent bimanual dexterity through an object-centric reward objective. By training policies in varying environments rather than tuning task-specific rewards, the system discovers behaviors such as pre-grasp manipulation, grasping, regrasping, in-hand adjustment, and handover without manual motion sequences. The distilled single policy successfully completes 61 of 80 trials (76.3%) on real hardware across four tasks with unseen objects and no fine-tuning. In simulated barcode scanning, it achieves an 85.0% success rate compared to 60.4% for two single-arm policies without handover. The work suggests that specifying object-level goals provides a scalable interface for learning general bimanual manipulation, shifting the burden from manual coordination design to defining desired object interactions.
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