Weakly-supervised Latent Models for Task-specific Visual-Language Control

• URL: http://arxiv.org/abs/2511.18319v1
• Date: Sun, 23 Nov 2025 07:18:28 GMT
• Title: Weakly-supervised Latent Models for Task-specific Visual-Language Control
• Authors: Xian Yeow Lee, Lasitha Vidyaratne, Gregory Sin, Ahmed Farahat, Chetan Gupta,
• Abstract summary: We propose a task-specific latent dynamics model that learns state-specific action-induced shifts in a shared latent space using only goal-state supervision.<n>In experiments, our approach achieves 71% success and generalizes to unseen images and instructions.
• Score: 2.10305808315957
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous inspection in hazardous environments requires AI agents that can interpret high-level goals and execute precise control. A key capability for such agents is spatial grounding, for example when a drone must center a detected object in its camera view to enable reliable inspection. While large language models provide a natural interface for specifying goals, using them directly for visual control achieves only 58\% success in this task. We envision that equipping agents with a world model as a tool would allow them to roll out candidate actions and perform better in spatially grounded settings, but conventional world models are data and compute intensive. To address this, we propose a task-specific latent dynamics model that learns state-specific action-induced shifts in a shared latent space using only goal-state supervision. The model leverages global action embeddings and complementary training losses to stabilize learning. In experiments, our approach achieves 71\% success and generalizes to unseen images and instructions, highlighting the potential of compact, domain-specific latent dynamics models for spatial alignment in autonomous inspection.

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