EAST: Embodied AI Safety Testing via Systematic Fault Injection in Digital Twins

Abstract

Task success is not a sufficient measure of safety for embodied artificial intelligence (AI), because robots may complete objectives while violating safety requirements. Existing evaluations often emphasize performance and robustness, but lack a systematic route from documented safety hazards to executable, verifiable tests. We present EAST (Embodied AI Safety Testing), a digital-twin framework that converts literature-grounded safety evidence into fault-injection tests across perception, instructions, environments, actions, and timing. EAST combines an expert-reviewed safety-fault taxonomy and eight executable operators with applicability and validity constraints. Its three-stage workflow generates test specifications, verifies fault realization during simulated execution, and independently assesses safety challenges, violations, task outcomes, and behavioral signatures using simulator-state evidence. The framework preserves execution traces and replayable records, supporting attribution and reproducibility across experiments. We evaluate EAST on six RoboTwin manipulation tasks and two policy architectures, ACT and RDT, against alternative testing strategies under matched budgets. Across five comparative campaigns, EAST targets all 74 admissible task-policy-operator combinations and obtains valid evidence for 63, while achieving safety-challenge exposure rates close to Random-Legal and Rule-Based. Moreover, 82 valid EAST executions complete their tasks despite safety violations, revealing risks concealed by success-oriented evaluation. These findings support systematic, validity-aware, reproducible safety testing beyond task-completion metrics.