2023年3月6日 · 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 ...

模型 palm-e 的输入有三种类型：文本、图像、连续状态（来自于机器人的各种传感器的观测结果）。输入中的连续状态和输入中的文本一样，映射到相同维度的向量空间中之后输入到模型中，至于如何映射在后面进行说明。

2023年10月13日 · PaLM-E是一个仅解码器的LLM,给定前缀或提示,它会自动回归地生成文本补全。我们称我们的模型为PaLM-E,因为我们使用PaLM(Chowdhery等,2022)作为预训练语言模型,并使其具身化。 PaLM-E的输入由文本和(多个)连续观测组成。

2024年1月9日 · 谷歌发布了一款被誉为史上“最强大脑”的人工智能模型PaLM-E (Parameter-efficient Language Model with Explicit Memory)，该模型能够从海量的语言数据中学习到更加精准和智能的语言处理能力PaLM-E的出现意味着机器人可以成为更多面手，更加有利于各种行业的应 …

2023年3月10日 · 3 月 6 日，Robotics at Google、柏林工业大学和 Google Research 团队提出了一个具身多模态语言模型 PaLM-E，该模型可以直接将现实世界的连续传感器模式纳入已经预训练好的 LLM 中，在单词和感知（words and percepts）之间建立联系，从而用于连续的机器人操作规 …

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 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.

2023年3月10日 · PaLM-E pushes the boundaries of how generally-capable models can be trained to simultaneously address vision, language and robotics while also being capable of transferring knowledge from vision and language to the robotics domain.

2023年7月23日 · 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.