arXiv AI
Jun 3
LAP: An Agent-to-Instrument Protocol for Autonomous Science
arXiv:2606. 03755v1 Announce Type: new Abstract: Autonomous science is moving from demonstration to infrastructure.
Making Self-Driving Laboratories Auditable:A Unified Framework for Hybrid Actions,Measurements, and Safe Execution
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.
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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.
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.
arXiv AI
Oct 15
Towards Long-horizon Embodied Agents with Tool-Aligned Vision-Language-Action Models
arXiv:2605.13119v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are effective robot action executors, but they remain limited on long-horizon tasks due to the dual burde...
arXiv AI
Aug 10
Representation Handoffs for OpenArm-Based Laboratory Mobile Manipulation
arXiv:2608. 07154v1 Announce Type: cross Abstract: Open-source robotics and foundation models have lowered the barrier to embodied AI, yet language-guided laboratory automation still requires reliable alignment from instructions and observations to safe actions.
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.
arXiv AI
Oct 19
From Natural Language Intent to Executable Control: Hierarchical Feedback-Guided LLM Planning for Multi-component Cyber-Physical Systems
The paper introduces a hierarchical feedback framework that guides large language models (LLMs) to produce executable plans for cyber‑physical systems (CPSs) from natural language commands. It prompts LLMs to generate both a structured task plan and signal temporal logic (STL) constraints, then uses a sound algorithm to detect contradictions and provide feedback for plan revision. At execution time, diagnostic information from failed control synthesis—such as violated constraints and conflicting component assignments—is fed back to the LLM to enable task replanning, achieving an 87.9% success rate on seven heterogeneous CPS scenarios.
arXiv Computer Vision
Oct 16
CarryOn: A Dual-Loop Harness for Personalized and Reliable Robot Assistance
CarryOn is a dual-loop system that enhances frozen vision‑language‑action (VLA) policies for robotic assistance by adding personalization and reliable execution. The planning loop maintains persistent user and scene context to ground subtask decomposition in user preferences, while the execution loop supervises policy rollouts to prevent hazardous contact and recover from failures using reusable control skills. Experiments on a real robot and in simulation show that CarryOn reduces safety violations from 52.6% to 6.1% and increases recovery success from 13% to 80%.
arXiv AI
Sep 28
A Safety-Bounded SDC-to-MCP Gateway for Medical AI Agents
The paper introduces a safety‑bounded gateway that translates IEEE 11073 Service‑Oriented Device Connectivity (SDC) into the Model Context Protocol (MCP) for medical AI agents. It exposes device metrics, alarms, context references, and semantic metadata as read‑only resources, while representing selected action affordances as policy‑validated dry‑run tools, ensuring that agent requests never trigger actual device operations. A Python prototype demonstrates fault and lifecycle experiments, deterministic baselines, and multi‑model agent evaluation, showing improved semantic conformity and preservation of the no‑execution boundary.
arXiv AI
Jul 22
SENTINEL: A Multi-Level Formal Framework for Safety Evaluation of Foundation Model-based Embodied Agents
arXiv:2510. 12985v3 Announce Type: replace Abstract: We present SENTINEL, a framework for formally evaluating the physical safety of foundation model (FM)-based embodied agents.
arXiv AI
Jun 30
BioProVLA-Agent: An Affordable, Protocol-Driven, Vision-Enhanced VLA-Enabled Embodied Multi-Agent System with Closed-Loop-Capable Reasoning for Biological Laboratory Manipulation
arXiv:2605. 07306v2 Announce Type: replace-cross Abstract: Biological laboratory automation can reduce repetitive manual work and improve reproducibility, but reliable embodied execution in wet-lab environments remains challenging.
arXiv AI
Jul 28
OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows
arXiv:2510. 24411v3 Announce Type: replace Abstract: Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms.