← Back to all news
arXiv AI By Zihan Zhang, Zeyu Wu, Xin Zhang, Mingyu Luo, Haohui Que, Tong Zhu

Making Self-Driving Laboratories Auditable:A Unified Framework for Hybrid Actions,Measurements, and Safe Execution

Read the original on arXiv AI →

The paper introduces a unified framework for self‑driving laboratories (SDLs) that integrates experimental planning, automated execution, and measurement feedback. It comprises four components: LAM, a measurement‑conditioned policy that generates robot trajectories and typed instrument API calls; OpenSDL, an execution substrate that maps operations to heterogeneous devices and records causal events; Safe‑SDL, a runtime gate that performs pre‑dispatch checks and monitors execution with signal temporal logic; and L3‑Bench, a benchmark protocol evaluating physical outcomes, timing, reproducibility, safety, and fault recovery. Together, these elements aim to address coordination and traceability challenges in SDL workflows.

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 AI.

Related stories

arXiv Machine Learning
Oct 5

Mind the Refinement Gap: When Safe High-Level Robot Plans Produce Unsafe Executions

The paper examines a trace‑completeness assumption in language‑enabled robot systems that combine semantic‑graph planning with temporal‑logic safety monitors. By auditing RoboGuard, the authors compare the monitor’s verdict on a surface plan with its verdict on a graph‑refined trace under the same LTL specification. Their evaluation of 28 controlled cases and 14 end‑to‑end scenarios shows that all targeted abstraction cases exhibit the predicted surface‑versus‑refined discrepancy, while controls behave as expected, suggesting that graph‑based trace refinement can serve as a lightweight mitigation and diagnostic tool for physical‑AI safety monitors.

By Stabak Das, Priyesh Ranjan, Xiangfang Li, Lijun Qian
roboticssafety
More like this →
arXiv AI
Jun 12

LabVLA: Grounding Vision-Language-Action Models in Scientific Laboratories

arXiv:2606. 13578v1 Announce Type: cross Abstract: Scientific laboratories increasingly rely on AI systems to reason about experiments, but the physical act of doing science remains largely outside their reach.

By Baochang Ren, Xinjie Liu, Xi Chen, Yanshuo Liu, Chenxi Li, Daqi Gao, Zeqin Su, Jintao Xing, Zirui Xue, Rui Li, Xiangyu Zhao, Shuofei Qiao, Minting Pan, Wangmeng Zuo, Lei Bai, Dongzhan Zhou, Ningyu Zhang, Huajun Chen
diffusionroboticsmultimodalbenchmarks
More like this →
arXiv AI
Sep 2

EmbodiedSkills: A Unified Framework for Orchestrating, Training, and Deploying VLA Agents

EmbodiedSkills is a unified framework that treats each skill decision as an execution proposal, checking prerequisites and verifying outcomes during long‑horizon vision‑language‑action tasks. It connects high‑level skill selection, bounded low‑level VLA execution, and post‑action verification through a fixed executable‑skill interface, enabling easy replacement of low‑level policies and recording of structured trajectories for supervision and adaptation. Instantiated with Qwen3‑VL and OpenPI/pi0.5 on RoboTwin 2.0 and LIBERO, the framework achieves high success rates (86.20% and 97.40% respectively) and demonstrates effective memory‑dependent task performance.

By Wei Wang, Wenqiao Zhang, Yutong Lin, Yuqian Yuan, Tianwei Lin, Jinhao Mao, Zhenxuan Fan, Mingjian Gao, Yang Dai, Wentong Li, Zheqi Lv, Zheng Dong, Yingjie Niu, Jiaqi Zhu, Jun Xiao, Chao Li, Yueting Zhuang
agentsroboticsmultimodal
More like this →