While Large Language Models (LLMs) excel at reasoning on text and Vision-Language Models (VLMs) are highly effective for visual perception, applying those models for visual instruction-based planning remains a widely open problem. In this paper, we introduce VIPER, a novel framework for multimodal instruction-based planning that integrates VLM-based perception with LLM-based reasoning. Our approach uses a modular pipeline where a frozen VLM generates textual descriptions of image observations, which are then processed by an LLM policy to predict actions based on the task goal. We fine-tune the reasoning module using behavioral cloning and reinforcement learning, improving our agent's decision-making capabilities. Experiments on the ALFWorld benchmark show that VIPER significantly outperforms state-of-the-art visual instruction-based planners while narrowing the gap with purely text-based oracles. By leveraging text as an intermediate representation, VIPER also enhances explainability, paving the way for a fine-grained analysis of perception and reasoning components.
@article{arxiv.2503.15108,
title = {VIPER: Visual Perception and Explainable Reasoning for Sequential Decision-Making},
author = {Mohamed Salim Aissi and Clemence Grislain and Mohamed Chetouani and Olivier Sigaud and Laure Soulier and Nicolas Thome},
journal= {arXiv preprint arXiv:2503.15108},
year = {2025}
}