PaLM-E

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Last updated: April 6, 2026

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What is PaLM-E?

PaLM-E is Google’s embodied multimodal language model that infuses a pre-trained PaLM decoder-only LLM with continuous observations—images, robot states, sensor streams, and neural 3D scene representations—by mapping them into the language embedding space, enabling unified text-generation for robotic manipulation planning, visual question answering, scene understanding, and other embodied reasoning tasks across multiple robot embodiments while retaining strong general language capabilities and achieving state-of-the-art results on visual-language benchmarks like OK-VQA.

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Machine Learning

PaLM-E's Top Features

Single embodied multimodal LLM up to 562B parameters (PaLM-E-562B).

Decoder-only autoregressive text generation based on PaLM.

Encodes images, robot states, sensor data, and neural 3D representations into the language embedding space.

Treats continuous observations as tokens in multimodal sentences for end-to-end training.

Embodied reasoning across multiple robot embodiments (tabletop and mobile manipulation).

Visual-language generalist performance with state-of-the-art results on OK-VQA and strong VQA/captioning.

Positive transfer via joint training on internet-scale language, vision, and visual-language data.

Zero-shot multimodal chain-of-thought reasoning for navigation, math on images, and egocentric Q&A.

Textual planning outputs executable by low-level robot policies or planners.

Special tokens for unambiguous object grounding and referencing in prompts.

Maintains strong language capabilities while adding multimodal and embodied skills.

Supports sensor fusion and reasoning in complex, dynamic physical environments.

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    Use Cases

    Robotics labs

    Design and execute long-horizon tabletop and mobile manipulation plans from multimodal observations and text prompts.

    Industrial automation teams

    Program robots via natural-language instructions for pick-and-place, sorting, or tool-use with object grounding tokens.

    Vision-language researchers

    Conduct VQA, captioning, and scene understanding experiments using a single model across datasets and modalities.

    Autonomous systems engineers

    Fuse images, states, and sensor data for embodied reasoning and decision-making in dynamic environments.

    HRI and dialog designers

    Enable natural-language dialogue with robots that converts user requests into executable stepwise action plans.

    AR/egocentric perception teams

    Perform zero-shot question answering and reasoning over temporally annotated egocentric video streams.

    Smart mobility developers

    Apply multimodal chain-of-thought reasoning to navigation questions like assessing route feasibility from images.

    Quality assurance & inspection

    Use VQA and captioning to check parts, describe scenes, and flag anomalies from multi-sensor inputs.

    Education & demo teams

    Create interactive demos that explain visual scenes, perform math on handwritten numbers, and plan embodied tasks.

    Cross-robot platform integrators

    Transfer policies and reasoning across multiple robot embodiments using a unified multimodal language model.

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