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Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework

Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03

Category: cs.AI · 人工智能 · first seen 2026-09-03

Abstract

Autonomous robots powered by deep learning face a fundamental auditability challenge: when incidents occur, investigators cannot reconstruct why the system made specific decisions. This paper presents TRACE (Transparent Reasoning Architecture for Credible Execution), a decision framework that ensures every autonomous action can be traced back to sensor evidence through documented causal chains. Th

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#202 most recent of 300 cs.AI papers we have recorded · ↑ newer: Discriminative World Models for Web Agents · ↓ older: Post-Training Language Models for Gold-Medal Performance in Coding Com
Cite this page: Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework: the #202 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/towards-trustworthy-autonomous-robots-an-explainable-ai-based-decision-framework.html
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