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English(EN) AffordSim: A Scalable Data Generator and Benchmark for Affordance-Aware Robotic Manipulation

新的AffordSim基准通过可供性预测增强机器人操作能力

研究人员开发了AffordSim,一个旨在提高机器人操作技能的新型数据生成器和基准。该系统将开放词汇3D可供性预测集成到模拟中,使机器人能够识别与特定任务相关的物体功能区域并与之接触。AffordSim合成特定任务的场景,在物体表面上进行可供性查询,并采样区域条件抓取,最终实现更有效的轨迹生成。该基准包含跨越各种操作技能和机器人形态的50个任务,展示了与手动标注相当的高成功率,并促进了到Franka FR3等真实机器人上的零样本迁移。 AI

影响 通过改进可供性感知任务的模拟数据生成,增强了机器人操作能力。

排序理由 该集群描述了一个在arXiv上发布的新型机器人操作基准和数据生成器。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的AffordSim基准通过可供性预测增强机器人操作能力

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该集群描述了一个在arXiv上发布的新型机器人操作基准和数据生成器。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mingyang Li, Haofan Xu, Haowen Sun, Xinzhe Chen, Sihua Ren, Liqi Huang, Xinyang Sui, Chenyang Miao, Jiawei Ye, Qiongjie Cui, Xingyu Chen, Zeyang Liu, Xuguang Lan ·

    AffordSim:一个可扩展的生成器和基准,用于可供性感知机器人操作

    arXiv:2604.11674v3 Announce Type: replace-cross Abstract: Many everyday robot manipulation skills are affordance-dependent, with success determined by whether the robot contacts the functional object region required by the subsequent action. Current simulation data generators obt…