In this paper, we introduce a method for unifying language, action, and state
information in a shared embedding space to facilitate a range of downstream
tasks in robot learning. Our method, Contrastive Language, Action, and State
Pre-training (CLASP), extends the CLIP formulation by incorporating
distributional learning, capturing the inherent complexities and one-to-many
relationships in behaviour-text alignment. By employing distributional outputs
for both text and behaviour encoders, our model effectively associates diverse
textual commands with a single behaviour and vice-versa. We demonstrate the
utility of our method for the following downstream tasks: zero-shot
text-behaviour retrieval, captioning unseen robot behaviours, and learning a
behaviour prior for language-conditioned reinforcement learning. Our
distributional encoders exhibit superior retrieval and captioning performance
on unseen datasets, and the ability to generate meaningful exploratory
behaviours from textual commands, capturing the intricate relationships between
language, action, and state. This work represents an initial step towards
developing a unified pre-trained model for robotics, with the potential to
generalise to a broad range of downstream tasks.

本文介绍了一种名为 Contrastive Language, Action, and State Pre-training (CLASP) 的方法，采用分布式输出使文本命令和行为单元之间的对齐变得更加准确，从而帮助了解决机器人学习中的相关问题。该模型在对未知数据集进行检索和图像描述生成等任务中表现出了优秀的性能。