title: Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon
    Robotic Manipulation
summary: 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.
paper_authors._id: 67bd3bcc797e4d53ce0bc70d
paper_authors.name: Yunhai Feng
paper_authors.status: claimed_verified
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paper_authors.fullname: Yunhai Feng
paper_authors.type: user
paper_authors.user: yunhaif
paper_authors._id: 67bd3bcc797e4d53ce0bc70e
paper_authors.name: Jiaming Han
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paper_authors.fullname: Jiaming Han
paper_authors.type: user
paper_authors.user: csuhan
paper_authors._id: 67bd3bcc797e4d53ce0bc70f
paper_authors.name: Zhuoran Yang
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paper_authors.fullname: Zhuoran Yang
paper_authors.type: user
paper_authors.user: zhuoranyang
paper_authors._id: 67bd3bcc797e4d53ce0bc710
paper_authors.name: Xiangyu Yue
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paper_authors.fullname: Xiangyu Yue
paper_authors.type: user
paper_authors.user: xyyue
paper_authors._id: 67bd3bcc797e4d53ce0bc711
paper_authors.name: Sergey Levine
paper_authors.status: admin_assigned
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paper_authors._id: 665ce54120a307a3754849dd
paper_authors.avatarUrl: /avatars/e698726e9be61dd50ce2efe372ed5dac.svg
paper_authors.fullname: Sergey Levine
paper_authors.type: user
paper_authors.user: svlevine
paper_authors._id: 67bd3bcc797e4d53ce0bc712
paper_authors.name: Jianlan Luo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:28:30.231Z
paper_authors._id: 64f8cb8ed04a890f5380d9a4
paper_authors.avatarUrl: /avatars/d6fdfdbb0c10141aa3b4c832d928121b.svg
paper_authors.fullname: Jianlan Luo
paper_authors.type: user
paper_authors.user: jianlanluo
paper_title: Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon
    Robotic Manipulation
paper_summary: 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.
paper_ai_summary: A novel test-time framework uses a "reflection" mechanism to enhance VLMs' physical reasoning and multi-stage manipulation capabilities.
paper_ai_keywords: vision-language models VLMs physical reasoning multi-stage manipulation generative model future world states action selection critical reflection Monte Carlo Tree Search MCTS