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

The description contains factual inaccuracies regarding the TRACE Framework. According to the provided search context, TRACE is a five-pillar (Trust, Review, Accountability, Critique, Explainability) framework, not a four-layer one, and it is specifically designed for multi-agent AI systems rather than exclusively for autonomous robots.

While the framework does ensure that every decision and tool invocation is traceable and auditable, it operates through a three-layer architecture comprising: 1. Governance & Compliance: Defines boundaries, sanitizes inputs, and enforces policy. 2. Operational Agents: Executes tasks using specialized agents coordinated by an Orchestrator. 3. Oversight & Assurance: Includes a dedicated Critic Agent for meta-validation and human-in-the-loop oversight.

TRACE aligns with global standards like the EU AI Act and NIST AI RMF, using an Overall System Confidence (OSC) score to dynamically govern automation thresholds and ensure policy-aligned, explainable behavior across varying degrees of agent autonomy.

Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework addresses a central gap in autonomous robotics: the need to make robot decisions not merely accurate, but inspectable, accountable, and explainable. The paper introduces TRACE, a four-layer auditable framework designed to connect each robot action back to the sensor evidence and intermediate reasoning steps that produced it. Rather than treating explainability as an after-the-fact visualization of a black-box policy, TRACE frames the robot’s decision process as a chain of explicit, traceable causal links from perception to action.

The key contribution is an architectural approach in which observations are treated as evidence, transformed through interpretable inference or decision logic, and mapped to control actions with a persistent audit record. Each layer preserves provenance information—such as the relevant sensor inputs, inferred states, applied constraints, and contributing factors—so that a downstream action can be reconstructed by following the causal chain backward. This makes it possible to answer questions like why did the robot choose this trajectory?, which sensor readings supported the decision?, and what alternative actions were considered or ruled out? In effect, TRACE turns explanation into a structural property of the system rather than a separate post-hoc diagnostic tool.

The work matters because it connects explainable AI with practical concerns in safety-critical robotics, including debugging, fault localization, human oversight, certification, and liability. If autonomous systems are to operate in environments where failure has serious consequences, stakeholders need more than high-level justifications; they need auditable evidence trails that can be examined by engineers, operators, regulators, or auditors. TRACE is significant because it provides a concrete design pattern for making autonomous decision-making inspectable at the level of individual actions, potentially enabling more trustworthy deployment of learned or hybrid perception-action systems in physical domains.