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palm_e [2026/03/25 15:25] – Create PaLM-E page: 562B embodied multimodal model agentpalm_e [2026/03/30 22:23] (current) – Restructure: footnotes as references agent
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 ====== PaLM-E: An Embodied Multimodal Language Model ====== ====== PaLM-E: An Embodied Multimodal Language Model ======
  
-**PaLM-E** is a **562 billion parameter** embodied multimodal language model introduced by Driess et al. (2023) at **Google**, combining vision, language, and robotic control in a single end-to-end trained model. With **2,457 citations**, it is one of the most impactful works bridging foundation models and robotics, demonstrating that a unified model can perform manipulation, navigation, visual question answering, and language tasks without task-specific fine-tuning.+**PaLM-E** is a **562 billion parameter** embodied multimodal language model introduced by Driess et al. (2023) at **Google**, combining vision, language, and robotic control in a single end-to-end trained model.((https://arxiv.org/abs/2303.03378|Driess et al. "PaLM-E: An Embodied Multimodal Language Model" (2023))) With **2,457 citations**, it is one of the most impactful works bridging foundation models and robotics, demonstrating that a unified model can perform manipulation, navigation, visual question answering, and language tasks without task-specific fine-tuning.
  
 [[https://arxiv.org/abs/2303.03378|arXiv:2303.03378]] [[https://arxiv.org/abs/2303.03378|arXiv:2303.03378]]
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 ===== Architecture ===== ===== Architecture =====
  
-PaLM-E injects continuous sensor observations directly into a pre-trained language model's embedding space. The largest variant combines:+PaLM-E injects continuous sensor observations directly into a pre-trained language model's embedding space. The largest variant combines:((https://arxiv.org/abs/2303.03378|Driess et al. "PaLM-E: An Embodied Multimodal Language Model" (2023)))
  
-  * **PaLM-540B**: A 540 billion parameter decoder-only language model +  * **PaLM-540B**: A 540 billion parameter decoder-only language model((https://arxiv.org/abs/2204.02311|Chowdhery et al. "PaLM: Scaling Language Modeling with Pathways" (2022))) 
-  * **ViT-22B**: A 22 billion parameter Vision Transformer encoder+  * **ViT-22B**: A 22 billion parameter Vision Transformer encoder((https://arxiv.org/abs/2302.05442|Dehghani et al. "Scaling Vision Transformers to 22 Billion Parameters" (2023)))
  
 Multimodal inputs are encoded as token-like embeddings that interleave with text tokens: Multimodal inputs are encoded as token-like embeddings that interleave with text tokens:
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   * **Object Scene Representations**: Encodes structured 3D scene features   * **Object Scene Representations**: Encodes structured 3D scene features
  
-The model processes **multimodal sentences** with text and sensor data in arbitrary order, generating text outputs that serve as high-level plans for robotic control.+The model processes **multimodal sentences** with text and sensor data in arbitrary order, generating text outputs that serve as high-level plans for robotic control.((https://palm-e.github.io|PaLM-E Project Page))
  
 ===== System Architecture ===== ===== System Architecture =====
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 ===== Key Results ===== ===== Key Results =====
  
-  * **State-of-the-art on OK-VQA** without task-specific fine-tuning+  * **State-of-the-art on OK-VQA** without task-specific fine-tuning((https://arxiv.org/abs/2303.03378|Driess et al. "PaLM-E: An Embodied Multimodal Language Model" (2023)))
   * Successfully controls real robots for long-horizon manipulation tasks   * Successfully controls real robots for long-horizon manipulation tasks
   * **Positive transfer**: Joint training on vision-language and robotics data improves both domains   * **Positive transfer**: Joint training on vision-language and robotics data improves both domains
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 | PaLM-E-562B | PaLM-540B | ViT-22B | 562B | | PaLM-E-562B | PaLM-540B | ViT-22B | 562B |
  
-===== References ===== 
- 
-  * [[https://arxiv.org/abs/2303.03378|Driess et al. "PaLM-E: An Embodied Multimodal Language Model" (2023)]] 
-  * [[https://palm-e.github.io|PaLM-E Project Page]] 
-  * [[https://arxiv.org/abs/2204.02311|Chowdhery et al. "PaLM: Scaling Language Modeling with Pathways" (2022)]] 
-  * [[https://arxiv.org/abs/2302.05442|Dehghani et al. "Scaling Vision Transformers to 22 Billion Parameters" (2023)]] 
  
 ===== See Also ===== ===== See Also =====
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   * [[chemcrow|ChemCrow: LLM Agent with Chemistry Tools]]   * [[chemcrow|ChemCrow: LLM Agent with Chemistry Tools]]
   * [[reasoning_via_planning|RAP: Reasoning via Planning]]   * [[reasoning_via_planning|RAP: Reasoning via Planning]]
 +
 +===== References =====
  
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