Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI

TL;DR

The paper introduces the SimBot Challenge, a new competition for university teams to develop embodied AI robot assistants in a simulated environment, expanding Alexa's conversational AI into multimodal and physical domains.

Contribution

It presents the design, infrastructure, and outcomes of the first SimBot Challenge, highlighting new opportunities for research in embodied AI and multimodal interaction.

Findings

Teams developed diverse approaches to embodied AI tasks

Performance analysis reveals strengths and limitations of current methods

Key lessons learned inform future embodied AI research

Abstract

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.