INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models

• URL: http://arxiv.org/abs/2510.01389v1
• Date: Wed, 01 Oct 2025 19:22:48 GMT
• Title: INSIGHT: INference-time Sequence Introspection for Generating Help Triggers in Vision-Language-Action Models
• Authors: Ulas Berk Karli, Ziyao Shangguan, Tesca FItzgerald,
• Abstract summary: 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.<n>We present textbfINSIGHT, a learning framework for leveraging token-level uncertainty signals to predict when a VLA should request help.
• Score: 2.509305596181814
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 transformer classifiers to map these sequences to help triggers. We explore supervision regimes for strong or weak supervision, and extensively compare them across in-distribution and out-of-distribution tasks. Our results show a trade-off: strong labels enable models to capture fine-grained uncertainty dynamics for reliable help detection, while weak labels, though noisier, still support competitive introspection when training and evaluation are aligned, offering a scalable path when dense annotation is impractical. Crucially, we find that modeling the temporal evolution of token-level uncertainty signals with transformers provides far greater predictive power than static sequence-level scores. This study provides the first systematic evaluation of uncertainty-based introspection in VLAs, opening future avenues for active learning and for real-time error mitigation through selective human intervention.

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