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

Cagri Temel

arXiv:2609.02861v1cs.ROcs.AI

TL;DR

Deep-learning robots lack preserved reasoning chains for reconstructing decisions after incidents, creating auditability and compliance challenges. TRACE embeds evidence links, causal records, counterfactuals, and execution monitoring into a four-layer architecture, achieving near-complete auditability in simulation. The evaluation reports 98.6% evidence traceability, 99.0% temporal continuity, and 98.1% decision reconstructability across 500 decision cycles.

  • Problem

    Deep-learning robots can make decisions whose reasoning cannot be reconstructed after incidents, limiting incident investigation and complicating safety and regulatory documentation.

  • Method

    TRACE embeds auditability into a model-agnostic four-layer decision architecture that links actions to sensor evidence, records alternatives and rationales, and verifies execution.

  • Results

    TRACE achieved 98.6% evidence traceability, 99.0% temporal continuity, and 98.1% decision reconstructability across 500 simulated decision cycles.

  • Takeaways & Limitations

    TRACE provides architectural auditability and decision-level artifacts that post-hoc XAI methods lack, while addressing high-risk AI logging requirements.

  • Takeaways & Limitations

    Evaluation used simulated sensors and controlled timing, so real hardware overhead and unmodeled synchronization, communication, and failure modes remain uncertain.

Abstract

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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. The framework organizes decision-making into four auditable layers: Semantic Perception for evidence-grounded entity recognition, Belief Reasoning for probabilistic state estimation with causal graphs, Action Synthesis for constraint-aware planning with counterfactual documentation, and Execution Verification for compliance monitoring. TRACE is model-agnostic yet designed to integrate learning-based perception modules (CNNs, transformers) while preserving decision-level auditability. We evaluate the framework using three objective metrics: Evidence Traceability (sensor-to-decision linkage), Decision Reconstructability (post-hoc analysis capability), and Temporal Continuity (audit trail completeness). Experimental evaluation on warehouse robot navigation demonstrates that TRACE achieves 98.6% evidence traceability, 99.0% temporal continuity, and 98.1% decision reconstructability across 500 simulated decision cycles. Post-hoc methods like LIME provide feature attributions but lack the artifact structure needed for decision-level reconstruction. The framework addresses EU AI Act requirements for high-risk system transparency and contributes to Explainable AI for safety-critical autonomous systems.

I. INTRODUCTION

TRACE addresses the auditability gap in deep-learning robots by embedding traceable evidence and causal records into autonomous decision-making. It is designed to support post-incident reconstruction while remaining compatible with learning-based models.

  • Motivation: Deep-learning robots can produce decisions whose sensor-to-action reasoning is not preserved for incident investigation.The paper frames this as a fundamental auditability challenge in deployed autonomous systems.
  • Motivation: Opaque decisions hinder incident investigation, safety certification, and compliance with EU AI Act logging requirements.The cited standards and regulation require documented risk assessment or logs that trace system functioning.
  • TRACE: TRACE remains model-agnostic while integrating CNN- and transformer-based perception and prediction modules with decision-level auditability.The framework is intended to remain applicable across current and future learning architectures.
  • TRACE: TRACE embeds audit requirements into the decision architecture so every action can be traced through documented causal chains to sensor evidence.This differs from extracting explanations only after an opaque model has produced an output.
  • Contributions: TRACE defines four-layer audit trails, three objective auditability metrics, counterfactual decision trees, and an evaluation reporting 98.6% evidence traceability.The contribution passage also reports sub-millisecond overhead and contrasts TRACE with post-hoc methods lacking decision-level artifacts.
  • Related work: LIME and SHAP explain individual predictions, while attention highlights inputs without establishing causal relationships or reconstructing temporal decision sequences.These limitations motivate decision-level artifact structures rather than isolated feature attributions.

B. Interpretable Control Policies

The paper contrasts interpretable policies and runtime verification with TRACE’s broader explainability criteria. TRACE is designed to combine real-time operation, end-to-end traceability, semantic explanations, and formal transparency metrics.

  • Interpretable Control Policies: Programmatic controllers can be human-readable and verifiable but typically sacrifice the performance that motivated neural networks.The paper identifies this trade-off as potentially unacceptable for complex robotic tasks.
  • Runtime Verification: Runtime verification detects specification violations and backup architectures can assume control, but these approaches do not explain how decisions were made.The comparison distinguishes behavioral constraint monitoring from decision-level explanation.
  • Comparison: TRACE is designed to satisfy real-time operation, end-to-end traceability, semantic-level explanations, and formal metrics for transparency assessment.These are the four criteria used in the paper’s comparison of explainability approaches.

III. TRACE FRAMEWORK ARCHITECTURE

TRACE decomposes autonomous decision-making into four layers that produce linked explanation artifacts. Semantic perception grounds entities in sensor evidence, while belief reasoning represents inferred states probabilistically through causal graphs.

  • Architecture: TRACE’s four layers each perform a specific function and produce explanation artifacts that feed the overall transparency assessment.The architecture is illustrated in Fig. 1.
  • Semantic Perception: Semantic Perception converts raw sensor data into structured entities while linking each detection to supporting sensor evidence.The layer outputs entity representations rather than detection results alone.
  • Semantic Perception: An evidence-grounded entity records identity, semantic class, estimated pose, calibrated confidence, and sensor evidence contributions.The evidence set includes sensor source, contribution weight, and spatial region.
  • Semantic Perception: The perception layer outputs an entity set with an Evidence Aggregation Record documenting contributing sensors and confidence.This preserves evidence provenance for each detection.
  • Belief Reasoning: Belief Reasoning maintains a probabilistic environment model and infers hidden variables such as pedestrian intentions or obstacle permanence.Inference operates over the entity set.
  • Belief Reasoning: A belief state is a probabilistic scene graph whose nodes represent entities or inferred states, relationships form edges, and probabilities quantify nodes and edges.The graph supports reasoning about partially observed environments.
  • Belief Reasoning: Causal graph construction incrementally updates entity nodes, infers relationship edges with conditional probabilities, and adds derived states linked to parent evidence.For example, an inferred blocked aisle is connected to its supporting evidence nodes.

C. Layer 3: Action Synthesis

Action Synthesis selects constrained actions while documenting alternatives and counterfactual triggers. Execution Verification then compares intended and actual behavior, preserving records for post-incident reconstruction.

  • C. Layer 3: Action Synthesis: The Action Synthesis Layer generates candidate actions, evaluates objectives and constraints, and selects the optimal choice.Its distinctive feature is explicit representation of alternatives and conditions that would trigger different selections.
  • C. Layer 3: Action Synthesis: A justified action records its command, alternative actions, selection rationale, and counterfactual conditions.These fields support analysis of why the selected action prevailed over alternatives.
  • C. Layer 3: Action Synthesis: The Counterfactual Decision Tree documents the selected path and conditions that would have triggered alternative actions.It supports questions about why the robot stopped and what would have made it continue.
  • D. Layer 4: Execution Verification: Execution Verification monitors execution, compares intended behavior with actual outcomes, and flags discrepancies.This layer maintains the audit trail during action execution.
  • D. Layer 4: Execution Verification: Timestamped audit records measure deviations between intended and actual behavior and enable complete reconstruction of decisions after incidents.The deviation measure is denoted δt in the framework’s record.

IV. AUDITABILITY METRICS

TRACE defines auditability metrics computed from logged execution artifacts, measuring evidence linkage and the completeness of causal documentation.

  • Three objective metrics measure whether decisions can be reconstructed by independent investigators from logged execution artifacts.The metrics are Evidence Traceability, Decision Reconstructability, and Temporal Continuity.
  • Evidence Traceability: Evidence Traceability measures whether each decision factor links through a documented chain to specific sensor evidence.
  • Evidence Traceability: Partial Traceability quantifies how much of an incomplete causal chain is documented when full Evidence Traceability is unavailable.A depth of 2 out of 3 means immediate and secondary causes are documented, but the root sensor observation is missing.
  • Computational complexity: Table II presents the theoretical computational complexity of each TRACE layer using detected entities, belief variables, candidate actions, and planning horizon.

B. Temporal Continuity

The auditability metrics capture both the continuity of recorded decisions and whether investigators can recover actions, alternatives, and rationales.

  • Temporal Continuity: Temporal Continuity measures the completeness of the audit trail across the analysis window.It accounts for intervals without recorded decisions.
  • Decision Reconstructability: Decision Reconstructability measures whether an independent engineer can determine the action taken, alternatives considered, and rationale for the selected action.
  • Decision Reconstructability: Decision Reconstructability is reported as the percentage of decision cycles satisfying all three reconstruction conditions jointly.

V. COMPLEXITY AND STORAGE ANALYSIS

TRACE produces explanation artifacts throughout execution, creating storage and scalability requirements that are addressed with tiered retention and projected compression.

  • Computational complexity: Each decision cycle generates explanation artifacts, with evidence aggregation scaling as O(n · s) and counterfactual trees requiring O(k · d) storage.Here, n is the number of detected entities, s the sensor modalities, k the candidate actions, and d the tree depth.
  • Fleet-scale storage: Tiered storage retains full recent detail, complete records for flagged decisions, summaries for routine operations, and compressed regulatory archives.
  • Compression: Lossless and delta compression are projected to reduce the 30 PB estimate to approximately 2–5 PB, though these projections require validation at scale.The projection combines 3–5× structural compression with an estimated 60–80% additional reduction from delta encoding.
  • Incident investigation: The tiered approach preserves full reconstructability for flagged decisions while retaining partial traceability and temporal context for routine operations.

VI. EXPERIMENTAL EVALUATION

The evaluation uses a reproducible warehouse-navigation simulation covering five scenarios, multimodal sensors, and realistic execution failure modes across 500 decision cycles.

  • Simulation environment: The simulation models a mobile robot navigating a 50m×30m warehouse with LiDAR, camera, and ultrasonic sensors at 10Hz.Camera observations are intermittently unavailable to simulate occlusion or sensor latency.
  • Scenarios: Five scenarios cover static clear paths, pedestrian crossings, safety-threshold proximity, multiple trajectories, and an approaching forklift.
  • Methodology: 500 decision cycles were executed using five scenarios with 100 cycles each and a fixed random seed for reproducibility.The simulation includes sensor unavailability, evidence corruption, causal-edge failures, rationale truncation, and counterfactual timeouts.

B. Results

TRACE achieved near-complete auditability across 500 simulated warehouse-navigation decision cycles, with high evidence traceability, temporal continuity, and decision reconstructability. Scenario performance varied with causal-chain sparsity, computational load, and decision complexity.

  • Overall auditability: 98.6±7.0% Evidence Traceability was achieved across 500 decision cycles.The 1.4% shortfall involved evidence-link corruption during sensor fusion or missing causal edges from write-timing issues.
  • Overall auditability: 99.0% Temporal Continuity was achieved, with five records affected by write timeouts during high-load scenarios.LIME and SHAP achieved 0% TC because they explain individual predictions without maintaining crossdecision state.
  • Overall auditability: 98.1% Decision Reconstructability was achieved, with 23 of 500 records not fully reconstructable.Incomplete records resulted from rationale-buffer overflow or counterfactual-generation timeouts, although actions and alternatives remained logged.
  • Per-scenario analysis: Evidence traceability ranged from 96.4% to 100% across scenarios, while temporal continuity and reconstructability varied with computational load and decision complexity.The reported range covered clear paths with sparse causal chains and forklift scenarios with straightforward sensor fusion.
  • Per-scenario analysis: A pedestrian detected at 1.5m triggered STOP, with logged collision risk 0.70, counterfactual alternatives, and a sensor-to-action causal graph.The graph linked LiDAR scan, point-cloud clustering, pedestrian classification, distance estimation, risk computation, and action selection.

VII. REGULATORY ALIGNMENT

TRACE is positioned as an architectural approach to regulatory and certification needs for auditable autonomous robots. The framework supplies decision-level artifacts, while its evaluation and deployment scope remain bounded by simulation, overhead, and domain limitations.

  • Regulatory alignment: TRACE provides concrete mechanisms for compliance verification by directly addressing auditability requirements emerging from AI regulation.Its architecture is intended to support verification rather than relying only on post-hoc explanations.
  • Regulatory alignment: TRACE’s audit trails provide evidence for ISO 13482 risk assessments and support reconstruction of decisions after incidents.The records can also help identify systematic failure patterns relevant to risk mitigation.
  • Certification implications: TRACE produces artifact structures for third-party audits without requiring access to proprietary model weights or training data.Independent reviewers can verify whether logged decisions align with safety specifications.
  • Comparison with post-hoc methods: TRACE’s architectural auditability substantially outperforms post-hoc methods because it documents decisions rather than only interpreting model outputs.Post-hoc methods operate on individual predictions and do not maintain the decision-level artifacts needed to reconstruct incident sequences.
  • Design trade-offs: 0.1ms per decision cycle represents approximately 1% of the computation budget at 10Hz, with overhead scaling linearly with entities and candidate actions.The overhead may become significant in environments containing many dynamic objects, although it remains bounded by the fixed cycle rate.
  • Limitations and future work: The evaluation used simulated sensors and warehouse navigation, while actual embedded-hardware overhead and other robotic domains require further validation.The authors also note that logged-artifact metrics do not establish whether investigators find reconstructions practically useful.

IX. CONCLUSION

TRACE makes autonomous robot behavior auditable through architectural mechanisms rather than post-hoc analysis, achieving high auditability across simulated decision cycles. The evaluation remains limited to simulation, so physical-hardware validation is still necessary before deployment conclusions.

  • TRACE makes autonomous robot behavior auditable through architectural mechanisms rather than post-hoc analysis.
  • 98.6% evidence traceability, 99.0% temporal continuity, and 98.1% decision reconstructability were demonstrated across 500 simulated decision cycles.The simulation included realistic failure modes across five scenarios.
  • Post-hoc XAI methods provide feature attributions but lack the artifact structure needed for decision-level reconstruction.TRACE therefore represents an architectural distinction from post-hoc explanation rather than merely a performance gap.
  • The evaluation was conducted entirely in simulation with modeled sensor noise and failure modes.Validation on physical hardware with real sensors remains necessary before deployment conclusions can be drawn.
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