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arxiv: 2303.03378 · v1 · pith:WDZCBQ7Onew · submitted 2023-03-06 · 💻 cs.LG · cs.AI· cs.RO

PaLM-E: An Embodied Multimodal Language Model

Danny Driess , Fei Xia , Mehdi S. M. Sajjadi , Corey Lynch , Aakanksha Chowdhery , Brian Ichter , Ayzaan Wahid , Jonathan Tompson
show 14 more authors
Quan Vuong Tianhe Yu Wenlong Huang Yevgen Chebotar Pierre Sermanet Daniel Duckworth Sergey Levine Vincent Vanhoucke Karol Hausman Marc Toussaint Klaus Greff Andy Zeng Igor Mordatch Pete Florence
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Pith reviewed 2026-05-10 22:25 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.RO
keywords embodied language modelmultimodal learningroboticsvisual question answeringlanguage groundingPaLM-Etransfer learningembodied AI
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The pith

One large model can plan robotic actions, answer visual questions, and caption images across different robot bodies by interleaving sensor data with language.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that large language models can be made to reason about the physical world by feeding them inputs that mix visual observations, robot state readings, and text, then training the whole system end-to-end. A sympathetic reader would care because this promises a single model that grounds words to real percepts without building separate systems for each task or each robot. The authors demonstrate the approach on manipulation planning, visual question answering, and captioning, using data from multiple sensor types and multiple robot platforms. They further report that training jointly on internet-scale language, vision, and robotics data produces positive transfer, and that the biggest version remains a capable general language and visual-language model.

Core claim

We propose embodied language models that directly incorporate real-world continuous sensor modalities into language models and thereby establish the link between words and percepts. Input to our embodied language model are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks including sequential robotic manipulation planning, visual question answering, and captioning. Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation

What carries the argument

Multi-modal sentences that interleave visual, continuous state estimation, and textual encodings, trained end-to-end with a pre-trained language model.

If this is right

  • Can perform sequential robotic manipulation planning from varied observation modalities.
  • Solves visual question answering and captioning as part of the same model.
  • Shows positive transfer when trained jointly on internet-scale language, vision, and embodied data.
  • Larger versions retain general language capabilities while reaching state-of-the-art on visual-language benchmarks like OK-VQA.
  • Works on multiple different robot embodiments without task-specific redesign.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same model could potentially interpret natural-language instructions while continuously updating its internal state from live sensors.
  • Joint training on internet data may allow embodied systems to improve by scaling rather than by hand-designing new modules for each domain.
  • The interleaving approach might extend to other continuous signals such as audio or force feedback in future embodiments.
  • Physical deployment on unstructured environments would test whether the learned grounding survives real sensor noise and longer task horizons.

Load-bearing premise

End-to-end training of interleaved visual, state, and text encodings with a pre-trained language model will create robust grounding between words and real-world percepts that generalizes across tasks, modalities, and robot embodiments without extra engineering.

What would settle it

A new robot embodiment or sensor type where the single trained model performs no better than separately engineered models, or shows no benefit from the joint language-vision-robotics training.

read the original abstract

Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establish the link between words and percepts. Input to our embodied language model are multi-modal sentences that interleave visual, continuous state estimation, and textual input encodings. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks including sequential robotic manipulation planning, visual question answering, and captioning. Our evaluations show that PaLM-E, a single large embodied multimodal model, can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits positive transfer: the model benefits from diverse joint training across internet-scale language, vision, and visual-language domains. Our largest model, PaLM-E-562B with 562B parameters, in addition to being trained on robotics tasks, is a visual-language generalist with state-of-the-art performance on OK-VQA, and retains generalist language capabilities with increasing scale.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 3 minor

Summary. The manuscript introduces PaLM-E, an embodied multimodal language model that directly incorporates real-world continuous sensor modalities (visual, state estimation) into a pre-trained PaLM LLM via interleaved input encodings. These encodings are trained end-to-end alongside the LLM on multiple tasks including sequential robotic manipulation planning, visual question answering, and captioning. The central claims are that a single model can address diverse embodied reasoning tasks across observation modalities and robot embodiments, exhibits positive transfer from joint training on internet-scale language/vision/visual-language data, and that the 562B-parameter variant achieves state-of-the-art on OK-VQA while retaining generalist language capabilities.

Significance. If the empirical results hold under rigorous scrutiny, this would be a significant contribution to embodied AI and multimodal learning. It provides evidence that scaling and joint training across internet-scale and embodied domains can produce generalist models capable of grounded reasoning without task-specific engineering, potentially influencing future work on bridging LLMs with robotics and real-world perception.

major comments (3)
  1. [Section 4] Section 4 (Experimental Setup and Results): The reported evaluations on robotic manipulation and embodied tasks omit full details on data splits, exact baseline implementations (including whether they use the same PaLM backbone), number of runs, and error bars. This weakens support for the claims of consistent outperformance and positive transfer, as the abstract and results sections present aggregate success metrics without these controls.
  2. [Section 3.2] Section 3.2 (Input Encoding): The description of how continuous state estimation is tokenized and interleaved with visual and textual encodings is incomplete (no explicit discretization scheme, embedding dimension, or normalization details). This is load-bearing for the grounding claim, as the weakest assumption in the paper is that end-to-end training of these encodings will robustly link words to percepts across embodiments.
  3. [Table 2] Table 2 / OK-VQA results: The state-of-the-art claim for PaLM-E-562B on OK-VQA lacks a complete set of recent multimodal baselines and an ablation isolating the contribution of the embodied robotics data versus the visual-language pretraining. Without this, it is unclear whether the embodied training is responsible for the reported gains or if they stem primarily from scale.
minor comments (3)
  1. [Abstract] The abstract and introduction use the term 'positive transfer' without a precise definition or quantitative metric (e.g., improvement over single-task training) in the summary paragraph.
  2. [Figure 1] Figure 1 (model diagram) would benefit from explicit callouts showing how continuous state values are converted to tokens and interleaved in the input sequence.
  3. [Section 3] Notation for the multimodal sentence construction (e.g., how visual patches and state vectors are denoted) is introduced without a consolidated table of symbols.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments, which have helped us identify areas where the manuscript can be strengthened. We address each major comment below and will incorporate the suggested revisions.

read point-by-point responses

  1. Referee: [Section 4] Section 4 (Experimental Setup and Results): The reported evaluations on robotic manipulation and embodied tasks omit full details on data splits, exact baseline implementations (including whether they use the same PaLM backbone), number of runs, and error bars. This weakens support for the claims of consistent outperformance and positive transfer, as the abstract and results sections present aggregate success metrics without these controls.

    Authors: We agree that greater transparency in the experimental protocol is needed to support the claims of outperformance and positive transfer. In the revised manuscript, we will expand Section 4 to include: (i) explicit descriptions of all data splits for robotic manipulation and embodied tasks, (ii) confirmation that baselines share the same PaLM backbone and implementation details, (iii) the number of independent runs performed, and (iv) error bars or standard deviations on all reported success rates. These additions will allow readers to better assess the reliability of the results. revision: yes

  2. Referee: [Section 3.2] Section 3.2 (Input Encoding): The description of how continuous state estimation is tokenized and interleaved with visual and textual encodings is incomplete (no explicit discretization scheme, embedding dimension, or normalization details). This is load-bearing for the grounding claim, as the weakest assumption in the paper is that end-to-end training of these encodings will robustly link words to percepts across embodiments.

    Authors: We acknowledge that the current description of state encoding in Section 3.2 lacks sufficient technical detail. We will revise this section to explicitly specify the discretization scheme applied to continuous state estimates (including binning method and resulting vocabulary size), the embedding dimensionality, and the normalization steps performed prior to interleaving with visual and text tokens. These clarifications will more rigorously support the grounding mechanism across embodiments. revision: yes

  3. Referee: [Table 2] Table 2 / OK-VQA results: The state-of-the-art claim for PaLM-E-562B on OK-VQA lacks a complete set of recent multimodal baselines and an ablation isolating the contribution of the embodied robotics data versus the visual-language pretraining. Without this, it is unclear whether the embodied training is responsible for the reported gains or if they stem primarily from scale.

    Authors: We partially concur. Table 2 already compares against the primary multimodal models available at the time of submission. To strengthen the presentation, we will add an ablation that directly compares PaLM-E variants trained with and without the embodied robotics data, thereby isolating the contribution of joint training beyond scale and visual-language pretraining. We will also incorporate any additional recent baselines that have appeared since submission, while noting that exhaustive coverage of every concurrent work is inherently limited by publication timelines. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper presents an empirical architecture and training procedure for PaLM-E, with all central claims (task performance, cross-modal transfer, embodiment generalization) resting on reported experimental results from new end-to-end training and evaluation rather than any closed-form derivation or self-referential definition. The pre-trained PaLM component is invoked as an external starting point whose parameters are not redefined or fitted inside the present work; no equation, prediction, or uniqueness claim reduces by construction to quantities already present in the inputs or prior self-citations. The derivation chain is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on standard deep learning assumptions and the pre-trained PaLM model. No new free parameters are explicitly introduced beyond standard training hyperparameters; no invented entities are postulated.

axioms (1)
  • domain assumption End-to-end training on interleaved multimodal inputs will establish effective grounding between language and percepts
    Invoked in the proposal of embodied language models and the training procedure described in the abstract.

pith-pipeline@v0.9.0 · 5600 in / 1197 out tokens · 52510 ms · 2026-05-10T22:25:05.231350+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

43 extracted references · 43 canonical work pages · cited by 205 Pith papers · 18 internal anchors

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