How Visible Are Silent Manipulation Failures? An Observability Study of False-Success Detection in Simulated Robot Episodes
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. A particularly damaging error is the false success: an episode the robot logs as a success when the task outcome was actually wrong. We ask a narrow but practical question about these episodes. Once an episode has already been flagged as a success, how much of the information needed to overturn that label is present in proprioception, and how muc
The proliferation of robot manipulation tasks in real-world scenarios necessitates more robust evaluation methods, and this research addresses a critical vulnerability in current imitation learning approaches.
Improving the reliability of success detection in robotic tasks is crucial for the safe and effective deployment of AI systems, particularly in sensitive applications and for accelerating robot learning.
This research provides a framework for understanding and mitigating 'false success' issues in robot learning, potentially leading to more reliable and trustworthy autonomous systems.
- · Robotics developers
- · AI safety researchers
- · Automation industries
- · Unreliable robot learning models
- · Brittle automation systems
Robot learning policies will become more robust and trustworthy.
Accelerated development and adoption of robot manipulation in complex environments.
Enhanced overall safety and reliability of AI-driven physical systems across various industries.
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