World Modeling Makes a Better Planner: Dual Preference Optimization for
  Embodied Task Planning
Recent advances in large vision-language models (LVLMs) have shown promise
for embodied task planning, yet they struggle with fundamental challenges like
dependency constraints and efficiency. Existing approaches either solely
optimize action selection or leverage world models during inference,
overlooking the benefits of learning to model the world as a way to enhance
planning capabilities. We propose Dual Preference Optimization (D^2PO), a new
learning framework that jointly optimizes state prediction and action selection
through preference learning, enabling LVLMs to understand environment dynamics
for better planning. To automatically collect trajectories and stepwise
preference data without human annotation, we introduce a tree search mechanism
for extensive exploration via trial-and-error. Extensive experiments on
VoTa-Bench demonstrate that our D^2PO-based method significantly outperforms
existing methods and GPT-4o when applied to Qwen2-VL (7B), LLaVA-1.6 (7B), and
LLaMA-3.2 (11B), achieving superior task success rates with more efficient
execution paths.
67d38a42d3d16e1166d81bed
Siyin Wang
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2025-03-14T08:56:44.686Z
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Siyin Wang
user
sinwang
67d38a42d3d16e1166d81bee
Zhaoye Fei
admin_assigned
2025-03-14T09:05:04.607Z
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Zhaoye Fei
user
ngc7293
67d38a42d3d16e1166d81bef
Qinyuan Cheng
claimed_verified
2025-06-24T08:13:30.247Z
63ec4715c81b6a52391c46b8
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QinyuanCheng
user
Cqy2019
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Shiduo Zhang
admin_assigned
2025-03-14T09:05:20.440Z
64196e45060a651c415d5cf7
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Shiduo Zhang
user
CyberDJ
67d38a42d3d16e1166d81bf1
Panpan Cai
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Jinlan Fu
admin_assigned
2025-03-14T09:05:38.981Z
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Fu Jinlan
user
Jinlan
67d38a42d3d16e1166d81bf3
Xipeng Qiu
admin_assigned
2025-03-14T09:05:46.041Z
61457b8deff2c9fdb4de4988
https://cdn-avatars.huggingface.co/v1/production/uploads/1632381702899-61457b8deff2c9fdb4de4988.jpeg
Xipeng Qiu
user
xpqiu
World Modeling Makes a Better Planner: Dual Preference Optimization for
  Embodied Task Planning
Recent advances in large vision-language models (LVLMs) have shown promise
for embodied task planning, yet they struggle with fundamental challenges like
dependency constraints and efficiency. Existing approaches either solely
optimize action selection or leverage world models during inference,
overlooking the benefits of learning to model the world as a way to enhance
planning capabilities. We propose Dual Preference Optimization (D^2PO), a new
learning framework that jointly optimizes state prediction and action selection
through preference learning, enabling LVLMs to understand environment dynamics
for better planning. To automatically collect trajectories and stepwise
preference data without human annotation, we introduce a tree search mechanism
for extensive exploration via trial-and-error. Extensive experiments on
VoTa-Bench demonstrate that our D^2PO-based method significantly outperforms
existing methods and GPT-4o when applied to Qwen2-VL (7B), LLaVA-1.6 (7B), and
LLaMA-3.2 (11B), achieving superior task success rates with more efficient
execution paths.
Dual Preference Optimization (D$^2$PO) enhances embodied task planning in large vision-language models by jointly optimizing state prediction and action selection using preference learning and tree search for efficient trajectory collection.
large vision-language models embodied task planning dependency constraints efficiency world models preference learning Dual Preference Optimization D$^2$PO state prediction action selection tree search VoTa-Bench Qwen2-VL LLaVA-1.6 LLaMA-3.2 task success rates execution paths