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Keywords {🔍}

robot, rtx, dataset, datasets, model, models, data, robotic, trained, open, xembodiment, skills, move, evaluation, robots, tasks, train, provide, labs, real, mixture, original, apple, small, learning, collaboration, contributing, paper, large, highcapacity, diverse, efficiently, applications, consolidation, pretrained, robotics, methods, xrobot, results, red, pepper, date, performing, academic, overview, embodiments, research, behaviors, objects, google,

Topics {✒️}

effective x-robot policies open x-embodiment dataset high-capacity models trained move red pepper high-capacity model trained large vision-language model open x-embodiment output robot actions original method constitutes original methods trained real robot evaluation single robot arms natural language tokens robot gripper frame shown remarkable successes exhibits positive transfer visually distinct scenes 7-dimensional vector consisting acknowledge yuheng kuang rochelle dela cruz standardized data formats dataset enrollment form robotic data mixture robotic learning datasets alongside experimental results performing diverse tasks robot embodiments bi-manual robots robotics data mixture models outperform rt-1 rt-1 model trained rt-2 model trained dataset trained pretrained models robot action robot pictures model overview common behaviors computer vision adapted efficiently google sheet biggest models robotic manipulation robotic control data sets dataset includes respective dataset dataset spreadsheet diverse datasets separate model

Questions {❓}

• Can such a consolidation happen in robotics?
• Can we instead train “generalist” X-robot policy that can be adapted efficiently to new robots, tasks, and environments?

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