Observing and Controlling Features in Vision-Language-Action Models

TL;DR

This paper introduces methods to observe and control features within Vision-Language-Action Models, enabling real-time, interpretable, and lightweight steering of robotic behaviors without the need for fine-tuning.

Contribution

It proposes the concepts of feature-observability and feature-controllability, providing techniques for linear observation and intervention in VLA internal representations.

Findings

Targeted linear interventions can steer robot behavior reliably.

VLAs have interpretable internal structures suitable for online adaptation.

Interventions preserve closed-loop capabilities during real-time control.

Abstract

Vision-Language-Action Models (VLAs) have shown remarkable progress towards embodied intelligence. While their architecture partially resembles that of Large Language Models (LLMs), VLAs exhibit higher complexity due to their multi-modal inputs/outputs and often hybrid nature of transformer and diffusion heads. This is part of the reason why insights from mechanistic interpretability in LLMs, which explain how the internal model representations relate to their output behavior, do not trivially transfer to VLA counterparts. In this work, we propose to close this gap by introducing and analyzing two main concepts: feature-observability and feature-controllability. In particular, we first study features that are linearly encoded in representation space, and show how they can be observed by means of a linear classifier. Then, we use a minimal linear intervention grounded in optimal control to accurately place internal representations and steer the VLA's output towards a desired region. Our results show that targeted, lightweight interventions can reliably steer a robot's behavior while preserving closed-loop capabilities. We demonstrate on different VLA architectures ($\pi_{0.5}$ and OpenVLA) through simulation experiments that VLAs possess interpretable internal structure amenable to online adaptation without fine-tuning, enabling real-time alignment with user preferences and task requirements.