Humans are excellent at understanding language and vision to accomplish a wide range of tasks. In contrast, creating general instruction-following embodied agents remains a difficult challenge. Prior work that uses pure language-only models lack visual grounding, making it difficult to connect language instructions with visual observations. On the other hand, methods that use pre-trained multimodal models typically come with divided language and visual representations, requiring designing specialized network architecture to fuse them together. We propose a simple yet effective model for robots to solve instruction-following tasks in vision-based environments. Our \ours method consists of a multimodal transformer that encodes visual observations and language instructions, and a transformer-based policy that predicts actions based on encoded representations. The multimodal transformer is pre-trained on millions of image-text pairs and natural language text, thereby producing generic cross-modal representations of observations and instructions. The transformer-based policy keeps track of the full history of observations and actions, and predicts actions autoregressively. Despite its simplicity, we show that this unified transformer model outperforms all state-of-the-art pre-trained or trained-from-scratch methods in both single-task and multi-task settings. Our model also shows better model scalability and generalization ability than prior work.

翻译：人类在理解语言和视觉以完成广泛任务方面表现出色。相比之下，构建通用的指令遵循具身智能体仍是一项艰巨挑战。先前使用纯语言模型的方法缺乏视觉基础，难以将语言指令与视觉观察联系起来。另一方面，使用预训练多模态模型的方法通常具有分离的语言和视觉表征，需要设计专门的网络架构进行融合。我们提出了一种简单而有效的机器人视觉环境指令遵循任务模型。所提方法包含一个编码视觉观察和语言指令的多模态Transformer，以及一个基于编码表征预测动作的Transformer策略。该多模态Transformer通过数百万图像-文本对和自然语言文本进行预训练，从而生成观察与指令的通用跨模态表征。基于Transformer的策略会跟踪完整的观察与动作历史，并以自回归方式预测动作。尽管模型结构简洁，但实验表明，这种统一的Transformer模型在单任务和多任务设置下均优于所有最先进的预训练或从头训练方法。该模型在可扩展性和泛化能力方面也优于先前工作。