{"ID":2872730,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.08820","arxiv_id":"2509.08820","title":"RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation","abstract":"Robotic chemists promise to both liberate human experts from repetitive tasks and accelerate scientific discovery, yet remain in their infancy. Chemical experiments involve long-horizon procedures over hazardous and deformable substances, where success requires not only task completion but also strict compliance with experimental norms. To address these challenges, we propose \\textit{RoboChemist}, a dual-loop framework that integrates Vision-Language Models (VLMs) with Vision-Language-Action (VLA) models. Unlike prior VLM-based systems (e.g., VoxPoser, ReKep) that rely on depth perception and struggle with transparent labware, and existing VLA systems (e.g., RDT, pi0) that lack semantic-level feedback for complex tasks, our method leverages a VLM to serve as (1) a planner to decompose tasks into primitive actions, (2) a visual prompt generator to guide VLA models, and (3) a monitor to assess task success and regulatory compliance. Notably, we introduce a VLA interface that accepts image-based visual targets from the VLM, enabling precise, goal-conditioned control. Our system successfully executes both primitive actions and complete multi-step chemistry protocols. Results show 23.57% higher average success rate and a 0.298 average increase in compliance rate over state-of-the-art VLA baselines, while also demonstrating strong generalization to objects and tasks.","short_abstract":"Robotic chemists promise to both liberate human experts from repetitive tasks and accelerate scientific discovery, yet remain in their infancy. Chemical experiments involve long-horizon procedures over hazardous and deformable substances, where success requires not only task completion but also strict compliance with e...","url_abs":"https://arxiv.org/abs/2509.08820","url_pdf":"https://arxiv.org/pdf/2509.08820v1","authors":"[\"Zongzheng Zhang\",\"Chenghao Yue\",\"Haobo Xu\",\"Minwen Liao\",\"Xianglin Qi\",\"Huan-ang Gao\",\"Ziwei Wang\",\"Hao Zhao\"]","published":"2025-09-10T17:52:09Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Language Model\"]","has_code":false}