Malvina Nikandrou


Demonstrating EMMA: Embodied MultiModal Agent for Language-guided Action Execution in 3D Simulated Environments
Alessandro Suglia | Bhathiya Hemanthage | Malvina Nikandrou | George Pantazopoulos | Amit Parekh | Arash Eshghi | Claudio Greco | Ioannis Konstas | Oliver Lemon | Verena Rieser
Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue

We demonstrate EMMA, an embodied multimodal agent which has been developed for the Alexa Prize SimBot challenge. The agent acts within a 3D simulated environment for household tasks. EMMA is a unified and multimodal generative model aimed at solving embodied tasks. In contrast to previous work, our approach treats multiple multimodal tasks as a single multimodal conditional text generation problem, where a model learns to output text given both language and visual input. Furthermore, we showcase that a single generative agent can solve tasks with visual inputs of varying length, such as answering questions about static images, or executing actions given a sequence of previous frames and dialogue utterances. The demo system will allow users to interact conversationally with EMMA in embodied dialogues in different 3D environments from the TEACh dataset.