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AI CURATED HOT STORY · 11/10/2026

Xi'an Jiaotong University Unveils AffordSim for Affordance-Driven Robot Data Generation

1 reports archived1 independent sourcesupdated 11/10/2026, 11:38:00
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A team led by Prof. Lan Xuguang at Xi'an Jiaotong University proposes AffordSim, which plugs open-vocabulary 3D affordance prediction into simulation-based trajectory generation: starting from natural-language task descriptions, it localizes functional regions on objects and then uses motion planning to produce executable robot manipulation demonstrations. The work also introduces a benchmark spanning 50 tasks, 7 manipulation skills and 5 robot arms, with code released openly, targeting the failure of generic grasping to preserve task-relevant alignment.

LATEST/A team led by Prof. Lan Xuguang at Xi'an Jiaotong University proposes AffordSim, which plugs open-vocabulary 3D affordance prediction into simulation-based trajectory generation: starting from natural-language task descriptions, it localizes functional regions on objects and then uses motion planning to produce executable robot manipulation demonstrations. The work also introduces a benchmark spanning 50 tasks, 7 manipulation skills and 5 robot arms, with code released openly, targeting the failure of generic grasping to preserve task-relevant alignment.

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  1. 新智元 (微信公众号)T2·72 pts

    Xi'an Jiaotong University Unveils AffordSim for Affordance-Driven Robot Data Generation

    • Open-vocabulary 3D affordance prediction constrains grasp selection so that later placement or hanging steps retain feasible space
    • Natural-language task descriptions drive scene and task organization, with motion planning producing executable demonstrations and cutting real-robot teleoperation cost
    • A benchmark of 50 tasks, 7 skill types and 5 robot arms over 500+ rigid and articulated objects is released alongside open-source code