Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon Robotic Manipulation
Yunhai Feng, Jiaming Han, Zhuoran Yang, Xiangyu Yue, Sergey Levine, Jianlan Luo
2025-02-25
Summary
This paper talks about a new way to make AI-powered robots better at doing complex tasks that involve multiple steps and understanding how objects in the real world behave
What's the problem?
Current AI models that can understand both images and language (called VLMs) are good at many things, but they struggle with planning long, complicated tasks for robots. They don't fully grasp how physical objects interact and can't plan far ahead enough to avoid making mistakes that add up over time
What's the solution?
The researchers created a system that helps VLMs 'think' better about physical tasks. It works by having the AI imagine what might happen next, use that information to choose what to do, and then think about how it could have done better. This process repeats, helping the AI improve its understanding and decision-making for complex robot tasks
Why it matters?
This matters because it could make robots much smarter and more capable of doing complicated jobs in the real world. Instead of just following simple instructions, robots using this system could handle tasks that require planning ahead and understanding how objects interact, like cooking a meal or organizing a cluttered room. This could lead to more helpful and versatile robots in homes, factories, and other places where complex manipulation tasks are needed
Abstract
Solving complex long-horizon robotic manipulation problems requires sophisticated high-level planning capabilities, the ability to reason about the physical world, and reactively choose appropriate motor skills. Vision-language models (VLMs) pretrained on Internet data could in principle offer a framework for tackling such problems. However, in their current form, VLMs lack both the nuanced understanding of intricate physics required for robotic manipulation and the ability to reason over long horizons to address error compounding issues. In this paper, we introduce a novel test-time computation framework that enhances VLMs' physical reasoning capabilities for multi-stage manipulation tasks. At its core, our approach iteratively improves a pretrained VLM with a "reflection" mechanism - it uses a generative model to imagine future world states, leverages these predictions to guide action selection, and critically reflects on potential suboptimalities to refine its reasoning. Experimental results demonstrate that our method significantly outperforms several state-of-the-art commercial VLMs as well as other post-training approaches such as Monte Carlo Tree Search (MCTS). Videos are available at https://reflect-vlm.github.io.