Action Hallucination in Generative Visual-Language-Action Models

2026-02-06

AI Summary

This work addresses the pervasive issue of action hallucination in generative vision-language-action models for robotic policy, which often violates physical constraints and leads to planning failures. It systematically uncovers the mechanistic origins of this problem, identifying three structural mismatches between latent variable models and feasible behaviors: topological, precision-related, and temporal. The study further elucidates an inherent trade-off between expressive capacity and reliability arising from these mismatches. By establishing a physics-aware constraint verification framework and a method for analyzing structural barriers, the paper proposes a principled approach to enhance policy trustworthiness without compromising expressiveness. This contribution provides both theoretical grounding and mechanistic insight into improving the robustness of generative robotic policies.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Robot Foundation Models such as Vision-Language-Action models are rapidly reshaping how robot policies are trained and deployed, replacing hand-designed planners with end-to-end generative action models. While these systems demonstrate impressive generalization, it remains unclear whether they fundamentally resolve the long-standing challenges of robotics. We address this question by analyzing action hallucinations that violate physical constraints and their extension to plan-level failures. Focusing on latent-variable generative policies, we show that hallucinations often arise from structural mismatches between feasible robot behavior and common model architectures. We study three such barriers -- topological, precision, and horizon -- and show how they impose unavoidable tradeoffs. Our analysis provides mechanistic explanations for reported empirical failures of generative robot policies and suggests principled directions for improving reliability and trustworthiness, without abandoning their expressive power.

Problem

• Action Hallucination
• Vision-Language-Action Models
• Physical Constraints
• Generative Policies
• Robot Foundation Models

Innovation

• action hallucination
• generative visual-language-action models
• structural mismatch
• topological barrier
• horizon barrier