RoboMIND is a large-scale multi-embodiment teleoperation dataset for robot manipulation containing 107k trajectories across four robots, with failure annotations and a digital twin simulator.
De- noising diffusion implicit models
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DP3 uses compact 3D representations from sparse point clouds inside diffusion policies to learn generalizable visuomotor skills from few demonstrations, reporting 24% gains in simulation and 85% success on real robots.
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RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation
RoboMIND is a large-scale multi-embodiment teleoperation dataset for robot manipulation containing 107k trajectories across four robots, with failure annotations and a digital twin simulator.
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3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations
DP3 uses compact 3D representations from sparse point clouds inside diffusion policies to learn generalizable visuomotor skills from few demonstrations, reporting 24% gains in simulation and 85% success on real robots.