arXiv:2607. 14826v1 Announce Type: cross Abstract: Safe physical AI for robot actions are required not only likely to succeed but tested to be safe before execution.
arXiv:2606. 29898v1 Announce Type: cross Abstract: Real-world evaluation is the gold standard for robot policies because it tests them against the physical conditions and deployment challenges they are ultimately designed to handle.
arXiv:2608. 07809v1 Announce Type: new Abstract: A world model is only useful for physical AI if it changes what the agent does, and only safe if it declines to do so when it is wrong.
arXiv:2607. 03177v1 Announce Type: cross Abstract: Traditional reinforcement learning (RL) for recovery in autonomous systems lacks causal understanding and generalizes poorly to novel failure scenarios.
arXiv:2608. 13719v1 Announce Type: new Abstract: Autonomous systems can fail in rare and heterogeneous ways, making real-world failure discovery difficult under limited testing budgets.
arXiv:2606. 08508v1 Announce Type: cross Abstract: Generative robot policies fail unpredictably at deployment: they hesitate at critical moments, drift off-task, or commit to unrecoverable actions.
arXiv:2607. 01111v1 Announce Type: cross Abstract: Robot policies inevitably encounter failures when deployed in real environments.
arXiv:2606. 08275v1 Announce Type: cross Abstract: When an LLM agent fails -- issues a refund it should not have, calls the wrong tool, leaks data -- existing tooling answers what happened (observability) or whether it passed (evaluation), but not which step caused the failure.
arXiv:2607. 14439v1 Announce Type: new Abstract: Generalist robot manipulation policies trained on large, diverse datasets have shown remarkable promise across a wide range of tasks.
arXiv:2510. 17640v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown strong manipulation capability when trained with large-scale imitation learning datasets.
arXiv:2606. 03134v1 Announce Type: cross Abstract: Imitation-learning policies for robot manipulation inherit the quality of the success labels attached to their training episodes, and those labels are usually produced by the robot's own success check.
arXiv:2608. 02958v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies trained by behavior cloning fail silently: from the action stream alone, a collapsing rollout looks much like one making clean progress, because imitation supplies no notion of progress.
arXiv:2602. 01515v2 Announce Type: replace-cross Abstract: Deploying learned control policies is risky because policies that appear robust in simulation can confidently enter out-of-distribution (OOD) states after Sim-to-Real transfer, causing silent failures and potential hardware damage.