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New robotics and physical-AI papers, with what each one means for the industry.

1 papers
arXiv
robustness benchmark for vision-language-action modelsIntermediate

LIBERO-Pro2.0: A Benchmark for Evaluating the Robustness of Embodied Foundation Models

Xueyang Zhou, Yangming Xu, Qianjiang Li, Guiyao Tie, Junhao Wu, Ziheng Huang, Guowen Zhang, Zhijie Yuan, Di Wu, Duanfeng Chu, Pan Zhou, Lichao Sun, Yongchao ChenHuazhong University of Science and Technology; Tsinghua University; Wuhan University of Technology; Lehigh University; University of Toronto

Seventeen robot control models that score 63.8% to 98.5% on the popular LIBERO benchmark lost an average of 26.5 percentage points when objects, goals, cameras or the robot itself were changed mid-task, according to a preprint from Huazhong University of Science and Technology and Tsinghua. Fine-tuning Physical Intelligence's pi0.5 on just 80 robustness demonstrations lifted its success on unseen perturbed tasks to 70.1%, against 28.0% to 47.9% for existing policies.

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Papers come from arXiv robotics feeds. Where our summary is not written yet, you see the opening lines of the abstract.