Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon
  Robotic Manipulation
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.
67bd3bcc797e4d53ce0bc70d
Yunhai Feng
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2025-02-25T09:39:31.085Z
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Yunhai Feng
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Jiaming Han
claimed_verified
2025-02-25T09:39:28.772Z
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Jiaming Han
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csuhan
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Zhuoran Yang
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2025-02-25T16:28:50.050Z
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Zhuoran Yang
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zhuoranyang
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Xiangyu Yue
admin_assigned
2025-02-25T16:28:43.087Z
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Xiangyu Yue
user
xyyue
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Sergey Levine
admin_assigned
2025-02-25T16:28:36.442Z
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Sergey Levine
user
svlevine
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Jianlan Luo
admin_assigned
2025-02-25T16:28:30.231Z
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Jianlan Luo
user
jianlanluo
Reflective Planning: Vision-Language Models for Multi-Stage Long-Horizon
  Robotic Manipulation
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.
A novel test-time framework uses a "reflection" mechanism to enhance VLMs' physical reasoning and multi-stage manipulation capabilities.
vision-language models VLMs physical reasoning multi-stage manipulation generative model future world states action selection critical reflection Monte Carlo Tree Search MCTS