Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for
  Embodied Interactive Tasks
Recent advances in deep thinking models have demonstrated remarkable
reasoning capabilities on mathematical and coding tasks. However, their
effectiveness in embodied domains which require continuous interaction with
environments through image action interleaved trajectories remains largely
-unexplored. We present Embodied Reasoner, a model that extends o1 style
reasoning to interactive embodied search tasks. Unlike mathematical reasoning
that relies primarily on logical deduction, embodied scenarios demand spatial
understanding, temporal reasoning, and ongoing self-reflection based on
interaction history. To address these challenges, we synthesize 9.3k coherent
Observation-Thought-Action trajectories containing 64k interactive images and
90k diverse thinking processes (analysis, spatial reasoning, reflection,
planning, and verification). We develop a three-stage training pipeline that
progressively enhances the model's capabilities through imitation learning,
self-exploration via rejection sampling, and self-correction through reflection
tuning. The evaluation shows that our model significantly outperforms those
advanced visual reasoning models, e.g., it exceeds OpenAI o1, o3-mini, and
Claude-3.7 by +9\%, 24\%, and +13\%. Analysis reveals our model exhibits fewer
repeated searches and logical inconsistencies, with particular advantages in
complex long-horizon tasks. Real-world environments also show our superiority
while exhibiting fewer repeated searches and logical inconsistency cases.
67e64ff17fe72aad5c26ab2f
Wenqi Zhang
admin_assigned
2025-03-28T09:10:00.794Z
6485bd278d14bcd5cdbb7c8d
/avatars/1427cf1a72b5db0cb263ad45885cf925.svg
Wenqi Zhang
user
zwq2018
67e64ff17fe72aad5c26ab30
Mengna Wang
67e64ff17fe72aad5c26ab31
Gangao Liu
admin_assigned
2025-03-28T09:09:24.111Z
64bb986ac733e8552fa4c5d1
/avatars/78dc5f802def0bdebd890438fcb1f966.svg
Gangao Liu
user
Gangao
67e64ff17fe72aad5c26ab32
Xu Huixin
67e64ff17fe72aad5c26ab33
Yiwei Jiang
admin_assigned
2025-03-28T09:09:05.352Z
60abbe1fe3de7c7440abb84d
https://cdn-avatars.huggingface.co/v1/production/uploads/1621868056572-noauth.jpeg
Yiwei Jiang
user
yijiang
67e64ff17fe72aad5c26ab34
Yongliang Shen
admin_assigned
2025-03-28T09:08:58.629Z
5e1058e9fcf41d740b69966d
/avatars/ce74839ba871f2b54313a670a233ba82.svg
Yongliang Shen
user
tricktreat
67e64ff17fe72aad5c26ab35
Guiyang Hou
admin_assigned
2025-03-28T09:08:43.400Z
67c03110e8c7d56a8e135ac8
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/eP3y_8_tyB8tcrT7py4L7.png
Hou
user
Guiyang1001
67e64ff17fe72aad5c26ab36
Zhe Zheng
67e64ff17fe72aad5c26ab37
Hang Zhang
67e64ff17fe72aad5c26ab38
Xin Li
67e64ff17fe72aad5c26ab39
Weiming Lu
67e64ff17fe72aad5c26ab3a
Peng Li
67e64ff17fe72aad5c26ab3b
Yueting Zhuang
Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for
  Embodied Interactive Tasks
Recent advances in deep thinking models have demonstrated remarkable
reasoning capabilities on mathematical and coding tasks. However, their
effectiveness in embodied domains which require continuous interaction with
environments through image action interleaved trajectories remains largely
-unexplored. We present Embodied Reasoner, a model that extends o1 style
reasoning to interactive embodied search tasks. Unlike mathematical reasoning
that relies primarily on logical deduction, embodied scenarios demand spatial
understanding, temporal reasoning, and ongoing self-reflection based on
interaction history. To address these challenges, we synthesize 9.3k coherent
Observation-Thought-Action trajectories containing 64k interactive images and
90k diverse thinking processes (analysis, spatial reasoning, reflection,
planning, and verification). We develop a three-stage training pipeline that
progressively enhances the model's capabilities through imitation learning,
self-exploration via rejection sampling, and self-correction through reflection
tuning. The evaluation shows that our model significantly outperforms those
advanced visual reasoning models, e.g., it exceeds OpenAI o1, o3-mini, and
Claude-3.7 by +9\%, 24\%, and +13\%. Analysis reveals our model exhibits fewer
repeated searches and logical inconsistencies, with particular advantages in
complex long-horizon tasks. Real-world environments also show our superiority
while exhibiting fewer repeated searches and logical inconsistency cases.
Embodied Reasoner, a deep learning model for interactive embodied tasks, outperforms existing visual reasoning models by leveraging observation-thought-action trajectories and a training pipeline that includes imitation learning, exploration, and reflection.
Embodied Reasoner observation-thought-action trajectories imitation learning rejection sampling reflection tuning visual reasoning models OpenAI o1 OpenAI o3-mini Claude-3.7