Robot-use Agents (RUAs) have shown promising zero-shot manipulation capabilities, but reliance on frontier, proprietary models limits research into further training. To democratize RUA research, we in
Uni-robot-agent enables open-weight robot-use agents to surpass proprietary models in zero-shot manipulation and suggests joint development of four core capabilities for future training.
Robot-use Agents (RUAs) have shown promising zero-shot manipulation capabilities, but reliance on frontier, proprietary models limits research into further training. To democratize RUA research, we introduce uni-robot-agent, a unified harness for competitive open-weight robot use that supports further training. Without privileged inputs, an out-of-the-box Qwen3.8-Flash-Next achieves a RoboDojo Score of 31.39, surpassing the published GPT-6 Astra-RoboProbe result of 28.97 and many VLA/WAM baselines, and performs competitively on 19 real-world tasks. Our analysis across model generations, families, and sizes links effective robot use to four general capabilities, suggesting that future RUA…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Democratizing Robot-Use Agents: A Unified Harness with Open Models was first seen on Hugging Face · Daily papers on Nov 3, 2026. Since then it has appeared in 12 checks on 2 different days. Its best position was #4 of 30, reached on Nov 3, 11:28 UTC. It dropped off the list after Nov 4, 03:28 UTC.
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It was first seen on Nov 3, 2026 and has been on the list on 2 days (12 checks) since.