Robo-MLT: Dual-System Robotic Manipulation for Long-Horizon Medical Laboratory Tasks
Overview
This research introduces Robo-MLT, a framework that couples explicit workflow monitoring with learned action generation to handle long-horizon medical laboratory tasks. The system uses a vision-language planner to compose atomic skill sequences and a multiframe execution monitor to detect task completion or infeasibility, triggering replanning when necessary. Evaluated on blood gas analysis and sample tube sorting with 50 nominal configurations per method and 20 sample-removal configurations, Robo-MLT achieves 82% and 70% task success rates respectively, outperforming baseline policies by 30 and 22 percentage points. The system demonstrates the ability to revise plans after operator intervention, such as removing a handled sample, indicating improved reliability and recoverability for complex laboratory workflows.
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Reported quantity · percentage points improvement over baseline: 30 other · Basis not reported · Differing source assertions
“ime coordinates it with the monitor. We evaluate Robo-MLT against four policy baselines and a VLM-based monitor baseline on blood gas analysis and blood sample tube sorting, with 50 nominal configurations per method and 20 sample-removal configurations. Robo-MLT achieves task success rates of 82\% and 70\%, exceeding $\pi_{0.5}$ by 30 and 22 percentage points, respectively. After an operator removes the sample being”
Exact source · revision 1Source owner not reported
Reported quantity · percentage points improvement over baseline: 22 other · Basis not reported · Differing source assertions
“ime coordinates it with the monitor. We evaluate Robo-MLT against four policy baselines and a VLM-based monitor baseline on blood gas analysis and blood sample tube sorting, with 50 nominal configurations per method and 20 sample-removal configurations. Robo-MLT achieves task success rates of 82\% and 70\%, exceeding $\pi_{0.5}$ by 30 and 22 percentage points, respectively. After an operator removes the sample being”
Exact source · revision 1Source owner not reported
Reported quantity · nominal configurations per method: 50 other · Basis not reported
“ime coordinates it with the monitor. We evaluate Robo-MLT against four policy baselines and a VLM-based monitor baseline on blood gas analysis and blood sample tube sorting, with 50 nominal configurations per method and 20 sample-removal configurations. Robo-MLT achieves task success rates of 82\% and 70\%, exceeding $\pi_{0.5}$ by 30 and 22 percentage points, respectively. After an operator removes the sample being”
Exact source · revision 1Source owner not reported
Reported quantity · sample-removal configurations: 20 other · Basis not reported
“ime coordinates it with the monitor. We evaluate Robo-MLT against four policy baselines and a VLM-based monitor baseline on blood gas analysis and blood sample tube sorting, with 50 nominal configurations per method and 20 sample-removal configurations. Robo-MLT achieves task success rates of 82\% and 70\%, exceeding $\pi_{0.5}$ by 30 and 22 percentage points, respectively. After an operator removes the sample being”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRobo-MLT: Dual-System Robotic Manipulation for Long-Horizon Medical Laboratory Tasks
This research introduces Robo-MLT, a framework that couples explicit workflow monitoring with learned action generation to handle long-horizon medical laboratory tasks. The system uses a vision-language planner to compose atomic skill sequences and a multiframe execution monitor to detect task completion or infeasibility, triggering replanning when necessary. Evaluated on blood gas analysis and sample tube sorting with 50 nominal configurations per method and 20 sample-removal configurations, Robo-MLT achieves 82% and 70% task success rates respectively, outperforming baseline policies by 30 and 22 percentage points. The system demonstrates the ability to revise plans after operator intervention, such as removing a handled sample, indicating improved reliability and recoverability for complex laboratory workflows.
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