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arXiv Computer Vision By Truman Hickok

SO-101 Bench: Accessible Spatial Manipulation

Read the original on arXiv Computer Vision →

SO-101 Bench is a simulated benchmark for language‑conditioned spatial manipulation on the low‑cost SO‑101 robotic arm, built using an Isaac Lab digital twin of a real setup. It features four tasks across five families, 138 objects with seen/unseen splits, 916 evaluation episodes, and 3,629 simulated demonstrations for policy fine‑tuning. The benchmark’s rankings mirror real‑world performance (Spearman ρ = 1.00) and reveals that while learned policies reach about 61% success, an LLM‑driven agent that writes code and delegates grasping achieves 83.2%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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