Effective grounding of pretrained VLM knowledge in robot behaviors

Determine effective mechanisms to ground the semantic and visual knowledge contained in pretrained vision-language models in concrete robot behaviors for general manipulation and control, so that high-level inferences can reliably translate into low-level action execution across diverse tasks and environments.

Background

The paper argues that pretrained vision-LLMs (VLMs) provide rich semantic and perceptual priors that could aid robot control, but that transferring this knowledge into executable behaviors is difficult. Prior hierarchical approaches typically pass natural language task instructions from a high-level VLM to a low-level policy, which limits the specificity and controllability of robot actions.

To address this bottleneck, the authors introduce Steerable Policies—vision-language-action models trained on synthetic, multi-level steering commands (e.g., subtasks, motions, and grounded pixel coordinates). This expanded interface is intended to better leverage pretrained VLM reasoning and in-context learning. The stated open challenge motivates their approach by highlighting the need for improved grounding methods to translate VLM capabilities into reliable robotic behaviors.