VLS: Steering Pretrained Robot Policies via Vision-Language Models

TL;DR

VLS is a training-free method that adapts pretrained robot policies at inference time using vision-language models to handle spatial and task variations without retraining.

Contribution

It introduces Vision-Language Steering (VLS), a novel inference-time control framework that guides pretrained policies using synthesized reward functions from vision-language models.

Findings

VLS achieves 31% improvement on CALVIN benchmark.

VLS attains 13% gain on LIBERO-PRO.

Demonstrated robust real-world adaptation on a Franka robot.

Abstract

Why do pretrained diffusion or flow-matching policies fail when the same task is performed near an obstacle, on a shifted support surface, or amid mild clutter? Such failures rarely reflect missing motor skills; instead, they expose a limitation of imitation learning under train-test shifts, where action generation is tightly coupled to training-specific spatial configurations and task specifications. Retraining or fine-tuning to address these failures is costly and conceptually misaligned, as the required behaviors already exist but cannot be selectively adapted at test time. We propose Vision-Language Steering (VLS), a training-free framework for inference-time adaptation of frozen generative robot policies. VLS treats adaptation as an inference-time control problem, steering the sampling process of a pretrained diffusion or flow-matching policy in response to out-of-distribution observation-language inputs without modifying policy parameters. By leveraging vision-language models to synthesize trajectory-differentiable reward functions, VLS guides denoising toward action trajectories that satisfy test-time spatial and task requirements. Across simulation and real-world evaluations, VLS consistently outperforms prior steering methods, achieving a 31% improvement on CALVIN and a 13% gain on LIBERO-PRO. Real-world deployment on a Franka robot further demonstrates robust inference-time adaptation under test-time spatial and semantic shifts. Project page: https://vision-language-steering.github.io/webpage/