Vision-language models pilot robots through a rehearsal harness, reaching 75.6% on LIBERO-Pro
single source· 1 articles · confidence: medium · first seen 2026-09-23 20:00 UTC
What this means for you
Nothing to act on yet. This is a preprint: it describes no released code, weights or price, and no evaluation date for the LIBERO-Pro figure, so the 75.6% cannot yet be checked against another run. If you build manipulation systems, the transferable part is the structure — letting the model rehearse an action in its own view before it acts.
A preprint posted on 23 September 2026 reports that a harness called World Action Agent lets a vision-language model — one that handles images and text together — drive a robot arm with simple tools. The model rehearses each action in a visual workspace before execution, then corrects residual error where it sees it. Skills learned from expert video and human teaching are retrieved by a separate skill agent. They report 75.6% average success on LIBERO-Pro, a manipulation benchmark, above their end-to-end and code-as-policy baselines; no evaluation date is given. Fine-tuning Qwen3.5-9B on the harness traces raised out-of-domain success from 1.7% to 43.3%.
Models in this story
Key facts
- ·The preprint reports 75.6% average success on LIBERO-Pro for World Action Agent using skills evolved only from LIBERO-90. source
- ·Fine-tuning Qwen3.5-9B on the harness's interaction traces raised its out-of-domain success from 1.7% to 43.3%. source
- ·The authors report that skills evolved from LIBERO-90 remained effective on robosuite without further learning. source
- ·The workspace has three named properties: contact views, action rehearsal and in-view correction. source
- ·The preprint is arXiv 2609.29964, posted 23 September 2026. source
What the sources say
- Hugging Face Daily Papers (research) — Single preprint describing the agent architecture, its learned skill library and the reported benchmark numbers.
Sources
The original reporting. Follow these — they did the work.
- Hugging Face Daily PapersWorld Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal2026-09-23