Mechanistic interpretability for steering vision-language-action models
Vision-Language-Action (VLA) models are a promising path to realizing
generalist embodied agents that can quickly adapt to new tasks, modalities, and
environments. However, methods for interpreting and steering VLAs fall far
short of classical robotics pipelines, which are grounded in explicit models of
kinematics, dynamics, and control. This lack of mechanistic insight is a
central challenge for deploying learned policies in real-world robotics, where
robustness and explainability are critical. Motivated by advances in
mechanistic interpretability for large language models, we introduce the first
framework for interpreting and steering VLAs via their internal
representations, enabling direct intervention in model behavior at inference
time. We project feedforward activations within transformer layers onto the
token embedding basis, identifying sparse semantic directions - such as speed
and direction - that are causally linked to action selection. Leveraging these
findings, we introduce a general-purpose activation steering method that
modulates behavior in real time, without fine-tuning, reward signals, or
environment interaction. We evaluate this method on two recent open-source
VLAs, Pi0 and OpenVLA, and demonstrate zero-shot behavioral control in
simulation (LIBERO) and on a physical robot (UR5). This work demonstrates that
interpretable components of embodied VLAs can be systematically harnessed for
control - establishing a new paradigm for transparent and steerable foundation
models in robotics.
68bf4715207285de11b07adf
Bear Häon
claimed_verified
2025-09-09T13:48:29.386Z
643a3857de858bcf53c30cd8
https://cdn-avatars.huggingface.co/v1/production/uploads/643a3857de858bcf53c30cd8/XQ7FtuOOOjx6PNLJh5uNM.jpeg
Bear Häon
user
bearhaon
68bf4715207285de11b07ae0
Kaylene Stocking
68bf4715207285de11b07ae1
Ian Chuang
68bf4715207285de11b07ae2
Claire Tomlin
Mechanistic interpretability for steering vision-language-action models
Vision-Language-Action (VLA) models are a promising path to realizing
generalist embodied agents that can quickly adapt to new tasks, modalities, and
environments. However, methods for interpreting and steering VLAs fall far
short of classical robotics pipelines, which are grounded in explicit models of
kinematics, dynamics, and control. This lack of mechanistic insight is a
central challenge for deploying learned policies in real-world robotics, where
robustness and explainability are critical. Motivated by advances in
mechanistic interpretability for large language models, we introduce the first
framework for interpreting and steering VLAs via their internal
representations, enabling direct intervention in model behavior at inference
time. We project feedforward activations within transformer layers onto the
token embedding basis, identifying sparse semantic directions - such as speed
and direction - that are causally linked to action selection. Leveraging these
findings, we introduce a general-purpose activation steering method that
modulates behavior in real time, without fine-tuning, reward signals, or
environment interaction. We evaluate this method on two recent open-source
VLAs, Pi0 and OpenVLA, and demonstrate zero-shot behavioral control in
simulation (LIBERO) and on a physical robot (UR5). This work demonstrates that
interpretable components of embodied VLAs can be systematically harnessed for
control - establishing a new paradigm for transparent and steerable foundation
models in robotics.
A framework for interpreting and steering Vision-Language-Action (VLA) models via internal representations enables real-time behavioral control without fine-tuning or environment interaction.
transformer layers token embedding basis sparse semantic directions activation steering method Pi0 OpenVLA LIBERO UR5