INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models
arXiv:2510.01389v2 Announce Type: replace-cross Abstract: Recent Vision-Language-Action (VLA) models show strong generalization capabilities, yet they lack introspective mechanisms for anticipating failures and requesting help from a human supervisor. We present \textbf{INSIGHT}, a learning framework for leveraging token-level uncertainty signals to predict when a VLA should request help. Using $\pi_0$-FAST as the underlying model, we extract per-token \emph{entropy}, \emph{log-probability}, and Dirichlet-based estimates of \emph{aleatoric and epistemic uncertainty}, and train compact transfor
The rapid advancement and growing complexity of Vision-Language-Action models necessitate robust mechanisms for error detection and human intervention to ensure safe and reliable deployment.
This research addresses a critical limitation of autonomous AI systems by enabling them to recognize their own limitations and proactively seek assistance, which is vital for real-world applications.
VLA models are shifting from purely autonomous operation to a more collaborative paradigm, where they can intelligently leverage human oversight, fundamentally altering their utility and safety profiles.
- · AI developers
- · Human-robot collaboration sectors
- · Safety-critical autonomous systems
- · Robotics
- · Tasks requiring perfect AI autonomy
- · AI systems lacking introspective capabilities
Increased reliability and trustworthiness of Vision-Language-Action models in deployment.
Accelerated integration of VLA models into sensitive applications requiring high levels of safety and human oversight.
The development of novel human-AI interaction paradigms where AI proactively manages its own limitations and requests specific forms of human help.
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