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New ATOM-Bench benchmark tests robotic manipulation generalization

Researchers have introduced ATOM-Bench, a new real-world benchmark designed to evaluate the atomic skills and compositional generalization capabilities of robotic manipulation policies. The benchmark includes 30 atomic tasks and 24 held-out compositional tasks, utilizing 3,000 human demonstrations for fine-tuning and evaluation. Initial tests on five representative policies revealed that while current models can grasp basic instruction-grounding, they struggle with fine-grained motor skills and reliably composing learned skills for novel tasks. AI

IMPACT This benchmark aims to improve the real-world generalization of robotic manipulation policies, addressing a key challenge in AI for robotics.

RANK_REASON The cluster describes a new academic benchmark and associated paper published on arXiv.

Read on arXiv cs.AI →

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New ATOM-Bench benchmark tests robotic manipulation generalization

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zenan Wu, Bingqing Wei, Lu Liu, Zheqi He, Xi Wang, Jiakang Liu, Zehui Li, Guocai Yao, Jing-Shu Zheng, Xi Yang, Yongtao Wang ·

    ATOM-Bench: A Real-World Benchmark for Atomic Skills and Compositional Generalization in Manipulation Policies

    arXiv:2606.16826v1 Announce Type: cross Abstract: Generalist manipulation policies are increasingly presented as foundation models for robotic control, but their real-world generalization remains difficult to diagnose. A policy may succeed on demonstrated tasks while still failin…

  2. arXiv cs.AI TIER_1 English(EN) · Yongtao Wang ·

    ATOM-Bench: A Real-World Benchmark for Atomic Skills and Compositional Generalization in Manipulation Policies

    Generalist manipulation policies are increasingly presented as foundation models for robotic control, but their real-world generalization remains difficult to diagnose. A policy may succeed on demonstrated tasks while still failing to execute fine-grained atomic skills or recombi…