Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released policy performs spatially constrained, language-conditioned tasks on them out of the box, an
A simulated benchmark for language-conditioned spatial manipulation on low-cost robot arms reveals that hybrid LLM agents outperform learned vision-language-action policies, with ranking consistency between simulation and real hardware.
Low-cost arms such as the SO-101 have become the most common entry point to robot learning, yet no released policy performs spatially constrained, language-conditioned tasks on them out of the box, and existing SO-101 benchmarks run only on physical hardware with simple tasks and few held-out objects. We introduce SO-101 Bench, a simulated benchmark for language-conditioned spatial manipulation built on an Isaac Lab digital twin of a real SO-101 setup. It has four tasks scored as five families, 138 objects in seen, unseen-instance and unseen-class splits, 916 evaluation episodes with subsets for held-out appearances, rotate-to-fit planning and occlusion memory, and 3,629 simulated…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
SO-101 Bench: Accessible Spatial Manipulation was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 36 checks on 4 different days. Its best position was #25 of 30, reached on Oct 30, 17:28 UTC. It dropped off the list after Nov 2, 07:28 UTC.
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Not at the moment. It was last on the list on Nov 2, 07:28 UTC, after 4 days of trending.
#25 on Hugging Face · Daily papers, on Oct 30, 17:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 4 days (36 checks) since.