The Alexa Prize program has empowered numerous university students to
explore, experiment, and showcase their talents in building conversational
agents through challenges like the SocialBot Grand Challenge and the TaskBot
Challenge. As conversational agents increasingly appear in multimodal and
embodied contexts, it is important to explore the affordances of conversational
interaction augmented with computer vision and physical embodiment. This paper
describes the SimBot Challenge, a new challenge in which university teams
compete to build robot assistants that complete tasks in a simulated physical
environment. This paper provides an overview of the SimBot Challenge, which
included both online and offline challenge phases. We describe the
infrastructure and support provided to the teams including Alexa Arena, the
simulated environment, and the ML toolkit provided to teams to accelerate their
building of vision and language models. We summarize the approaches the
participating teams took to overcome research challenges and extract key
lessons learned. Finally, we provide analysis of the performance of the
competing SimBots during the competition.

这篇论文介绍了 SimBot Challenge 这个新挑战赛，其中大学团队竞争建立在模拟物理环境中完成任务的机器人助手。论文概述了 SimBot Challenge，包括线上和线下挑战阶段。我们描述了为团队提供的基础设施和支持，包括 Alexa Arena，模拟环境以及为团队加速构建视觉和语言模型而提供的 ML 工具包。我们总结了参赛团队克服研究挑战所采取的方法，并提取了关键的经验教训。最后，我们对比赛期间参赛 SimBot 的表现进行了分析。