mlsanityData to 7 Oct 2026

Introspective Perception: Learning to Predict Failures in Vision Systems

arXiv 1607.08665 ↗First posted 28 Jul 2016cs.RO cs.AI cs.CVcited by 17

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Abstract (arXiv)

As robots aspire for long-term autonomous operations in complex dynamic environments, the ability to reliably take mission-critical decisions in ambiguous situations becomes critical. This motivates the need to build systems that have situational awareness to assess how qualified they are at that moment to make a decision. We call this self-evaluating capability as introspection. In this paper, we take a small step in this direction and propose a generic framework for introspective behavior in perception systems. Our goal is to learn a model to reliably predict failures in a given system, with respect to a task, directly from input sensor data. We present this in the context of vision-based autonomous MAV flight in outdoor natural environments, and show that it effectively handles uncertain situations.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jul 2016.

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Citing papers per year: 2021: 7; 2022: 0; 2023: 3; 2024: 3; 2025: 1; 2026: 3.

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  4. 2026Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaborationcited by 0
  5. 2026Auditing Latent-Space Monitors for Autonomous Drivingcited by 0