HuggingFace Daily Papers（社区热门论文）· HuggingFace Daily Papers（社区热门论文）·· 5 天前AI 评分40
GPT-6 Astra 机器人智能体：成功率提升 14%，Token 用量减少 65%
AI 导读
PyRUA-Lean 框架让 GPT-6 Astra 驱动的机器人智能体在同等 LLM 调用预算下，将 LIBERO-PRO、RoboTwin 2.0 和 RoboCasa365 共 700 个仿真任务的整体成功率从 63.1% 提升至 71.7%。
正文
Authors:Ruiyang Si, Jianxin Bi, Shunyu Yang, Rui Ni, Wenbo Huang, Qiang Wang, Shulong Jiang, Duomin Wang, Xiuyu Li, Haiwen Feng, Zhen Dong, Daquan Zhou
Abstract:Vision language model (VLM) agents can control robots through visual feedback and action primitives, but repeated model invocations and redundant observations incur substantial token overhead. We introduce PyRUA-Lean, an interactive code-execution framework that couples feedback-driven primitive composition with selective observation: the agent composes classical robot primitives and learned vision-language-action (VLA) policies into Python cells that perform conditional checks and local retries, returning only explicitly requested images and state feedback for replanning. Across 700 simulated task instances from LIBERO-PRO, RoboTwin 2.0, and RoboCasa365, we compare PyRUA-Lean with a tool-calling baseline using the same GPT-6 Astra planner and underlying robot primitives. Under equal LLM-call budgets, PyRUA-Lean increases overall success from 63.1% to 71.7%. On instances solved by both agents, it uses 49% fewer LLM calls and 65% fewer input tokens.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2610.01939 [cs.CV] |
| (or arXiv:2610.01939v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01939 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ruiyang Si [view email]
[v1]
Thu, 1 Oct 2026 16:07:49 UTC (2,932 KB)
来源：HuggingFace Daily Papers（社区热门论文） · arxiv.org