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.
By Rohan Bhagra, Mahantesh Halapannavar, Uddhav Bhattarai
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...
By Ruiyang Wang, Hao-Lun Hsu, Haocheng Meng, Songqiao Xie, Miroslav Pajic
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.
By Cagri Temel
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.
By Haimin Hu
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...
By {\L}ukasz Sobczak, Nur Kele\c{s}o\u{g}lu, S{\l}awomir Piotr Nowak
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.
By Weizhe Xu, Jialiang Fan, Mengyu Liu, Fanxin Kong