arXiv AI
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation
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arXiv AI
Sep 11
CT-SAFR: Safe and Interpretable Chain-of-Thought Reasoning for Autonomous Robots: A Multi-Layered Verification Framework for Trustworthy AI-Driven Robotic Decision Making
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 AI
Jun 2
Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics
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 AI
Sep 18
CoreSense: Traceable Failure Recall and Conflict-Aware Belief Gating for Auditable Robot Decisions
CoreSense is a robot‑system integration architecture that traces episodic evidence and uses a conflict‑aware belief gate to decide whether to proceed, re‑observe, abstain, or escalates. The gate evaluates scope, provenance, time, contradiction, and support before making a recommendation. Evaluation on public robot datasets, simulations, and a live cloud deployment shows that belief gating can eliminate protocol‑defined unsafe proceeds while maintaining auditability.
arXiv AI
Sep 3
Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework
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 AI
Aug 11
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
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 AI
Jun 11
CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy
arXiv:2606. 12352v1 Announce Type: cross Abstract: Multi-robot collaboration allows robots to efficiently take on a wide range of tasks, from moving a couch through a doorway to assembling structures on a construction site.