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On October 22, Zhiyuan officially open-sourced the AGIBOT WORLD 2026 fourth installment Physical Reasoning dataset. The task design of this dataset shifts its focus from manipulation difficulty to decision-making difficulty, constructing different state scenarios of the same task around factors such as object count, occlusion relationships, position occupancy, and task progress, covering nine core reasoning capabilities including attribute selection, conditional judgment, manipulation feasibility assessment, target search, long-horizon planning, and process memory, and setting three difficulty levels: Easy, Medium, and Hard. The data describes goals through task instructions, characterizes task structure through task stages, subtask intervals, and action steps, and provides action descriptions as well as spatial annotations such as boxes, points, and lines. According to a report by Shangguan News, the dataset will be used for training Zhiyuan's upcoming Agent framework and new-generation foundation model. No further progress has been disclosed at present.
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AgiBot Open-Sources AGIBOT WORLD 2026 Phase 4 Embodied Reasoning DatasetDevelopments
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Zhiyuan open-sources AGIBOT WORLD 2026 fourth installment embodied reasoning dataset
Source:Shangguan News
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Oct 22
Shanghai Observer
AgiBot Open-Sources AGIBOT WORLD 2026 Phase 4 Embodied Reasoning Dataset
On October 22, AgiBot officially open-sourced the AGIBOT WORLD 2026 Phase 4 Physical Reasoning dataset, with task design shifting its focus from manipulation difficulty to decision-making difficulty. The dataset constructs different state scenarios of the same task around factors such as object count, occlusion relationships, position occupancy, and task progress, covering nine core reasoning capabilities including attribute selection, conditional judgment, manipulation feasibility assessment, target search, long-horizon planning, and process memory, and sets three difficulty levels: Easy, Medium, and Hard. The data describes goals through task instructions, characterizes task structure through task stages, subtask intervals, and action steps, and provides action descriptions along with spatial annotations such as boxes, points, and lines. It will be used to train AgiBot's upcoming Agent framework and new-generation foundation model.
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