CT‑SAFR is a multi‑layered verification framework designed to enhance the safety and faithfulness of Chain‑of‑Thought reasoning in autonomous robots. The framework achieves a 94.2% hallucination detection rate with sub‑500 ms latency, and a warehouse robot case study shows an 87% reduction in unsafe reasoning outputs. The study also offers recommendations for responsible deployment of reasoning‑capable autonomous robots.
arXiv:2608. 09857v1 Announce Type: cross Abstract: Advances in advanced artificial intelligence tools have sparked research in robot autonomy, but the development of such systems has largely focused on execution rather than verifying the feasibility actions planning models propose.
arXiv:2610.14530v1 Announce Type: cross Abstract: Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tas...
The paper introduces TRACE, a Transparent Reasoning Architecture for Credible Execution, which provides an explainable AI-based decision framework for autonomous robots. TRACE structures decision-making into four auditable layers—Semantic Perception, Belief Reasoning, Action Synthesis, and Execution Verification—to ensure every action can be traced back to sensor evidence through documented causal chains. Experimental results on warehouse robot navigation show high evidence traceability (98.6%), temporal continuity (99.0%), and decision reconstructability (98.1%) across 500 simulated decision cycles.
arXiv:2606. 02562v1 Announce Type: cross Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals, competency, and willingness to cooperate.
arXiv:2609.37554v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructi...
SafeInferCom introduces an inference-time monitor that verifies intermediate plans generated by large reasoning language models (LRLMs) for robotic task planning, without disrupting the decoding trajectory. The framework uses a formal verifier to preserve valid intermediate plans and guide error correction during generation, improving planning success and speeding up error correction compared to one-shot inference. Experiments across multiple LRLMs and planning domains, including VirtualHome and a real-world robotic-arm demonstration, show that SafeInferCom enhances success rates while reducing token usage when combined with iterative refinement.
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:2608. 03745v1 Announce Type: new Abstract: Chain-of-Thought (CoT) reasoning offers a promising window into model monitoring.
The paper introduces ARMOR, an Adaptive Round-based Multi-task Model designed for robotic failure detection and reasoning. It treats detection and reasoning as a self‑refinement process, iteratively predicting outcomes and natural language explanations conditioned on prior outputs. Training leverages both large sparse binary labels and small rich reasoning annotations through offline and online imitation learning, while inference uses multiple refinement trajectories and a self‑certainty metric to select the best prediction.
The paper introduces TRUST, a token‑level reward model that predicts the correctness of partial chain‑of‑thought (CoT) traces in vision‑language‑action (VLA) policies, enabling monitoring and selective steering of reasoning. On driving and manipulation VLA benchmarks, TRUST improves reasoning accuracy and reduces collision rates and trajectory errors, though its impact on overall task performance varies across tasks. The study defines two evaluation axes—correctability and actionability—to assess when CoT can serve as a runtime safety interface.
arXiv:2606. 23754v1 Announce Type: cross Abstract: Deploying foundation models for robot control raises a central challenge: the expressive power that enables rich, multimodal perception also makes these models opaque and difficult to analyze formally, rendering them intractable for existing verification tools.
The paper introduces a ROS-Agent architecture that enhances task reliability and execution efficiency for open‑source LLM‑powered robotic agents. It adds a MetaTool that forces the LLM to produce a structured pseudo‑code plan before any action, storing this plan in a scratchpad to separate planning from execution. Experiments on a custom mobile robot show up to ~24% improvement in complex task completion and contextual consistency compared to the baseline.