RLBench is an ambitious large-scale benchmark and learning environment designed to facilitate research in a number of vision-guided manipulation research areas, including: reinforcement learning, imitation learning, multi-task learning, geometric computer vision, and in particular, few-shot learning.
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ManiSkill2 is the next generation of the SAPIEN ManiSkill benchmark, to address critical pain points often encountered by researchers when using benchmarks for generalizable manipulation skills. It includes 20 manipulation task families with 2000+ object models and 4M+ demonstration frames, which cover stationary/mobile-base, single/dual-arm, and rigid/soft-body manipulation tasks with 2D/3D input data simulated by fully dynamic engines.
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The YCB-Slide dataset comprises of DIGIT sliding interactions on YCB objects. We envision this can contribute towards efforts in tactile localization, mapping, object understanding, and learning dynamics models. We provide access to DIGIT images, sensor poses, RGB video feed, ground-truth mesh models, and ground-truth heightmaps + contact masks (simulation only). This dataset is supplementary to the MidasTouch paper, a CoRL 2022 submission.
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