Mar 6, 2023 · 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.

PaLM-E is a decoder-only LLM that generates textual completions autoregressively given a prefix or prompt. We call our model PaLM-E, since we use PaLM (Chowdhery et al., 2022) as the pre-trained language model, and make it Embodied.

This is the open source implementation of the SOTA multi-modality foundation model "PALM-E: An Embodied Multimodal Language Model" from Google, PALM-E is a single large embodied multimodal model, that can address a variety of embodied reasoning tasks, from a variety of observation modalities, on multiple embodiments, and further, exhibits ...

PaLM-E是一种仅用于解码器的LLM，它在给定前缀或提示的情况下自动生成文本补全。称论文的模型为PaLM-E，因为论文使用PaLM（Chowdhery等人，2022）作为预训练语言模型，并使其具体化。 PaLM-E的输入包括文本和（多个）连续观察。对应于这些观察的多模态标记与文本 ...

Jul 23, 2023 · 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 internetscale language, vision, and visual-language domains.

PaLM-E operates on multimodal sentences, i.e. sequences of tokens where inputs from arbitrary modalities (e.g. images, neural 3D representations, or states, in green and blue) are inserted alongside text tokens (in orange) as input to an LLM, trained end-to-end.

Mar 7, 2023 · On Monday, a group of AI researchers from Google and the Technical University of Berlin unveiled PaLM-E, a multimodal embodied visual-language model (VLM) with 562 billion parameters that...

Mar 7, 2023 · The largest PaLM-E model is capable of processing PaLM-level natural language while also understanding and describing image content and guiding robots through precise, sequential steps by combining language and computer vision.

PaLM-E pushes the boundaries of how generally-capable models can be trained to simultaneously address vision, language, and robotics, and might be a key enabler to other broader applications using multimodal learning.

Nov 29, 2023 · What’s new: Danny Driess and colleagues at Google and Technische Universität Berlin proposed PaLM-E, a large multimodal model designed to help control robots. PaLM-E takes a text command, and in executing the command, uses sensor data from a robot to resolve it into a series of low-level subcommands.