The VoxWorld Platform for Multimodal Embodied Agents
Nikhil Krishnaswamy, William Pickard, Brittany Cates, Nathaniel Blanchard, James Pustejovsky
Abstract
We present a five-year retrospective on the development of the VoxWorld platform, first introduced as a multimodal platform for modeling motion language, that has evolved into a platform for rapidly building and deploying embodied agents with contextual and situational awareness, capable of interacting with humans in multiple modalities, and exploring their environments. In particular, we discuss the evolution from the theoretical underpinnings of the VoxML modeling language to a platform that accommodates both neural and symbolic inputs to build agents capable of multimodal interaction and hybrid reasoning. We focus on three distinct agent implementations and the functionality needed to accommodate all of them: Diana, a virtual collaborative agent; Kirby, a mobile robot; and BabyBAW, an agent who self-guides its own exploration of the world.- Anthology ID:
- 2022.lrec-1.164
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1529–1541
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.164
- DOI:
- Cite (ACL):
- Nikhil Krishnaswamy, William Pickard, Brittany Cates, Nathaniel Blanchard, and James Pustejovsky. 2022. The VoxWorld Platform for Multimodal Embodied Agents. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1529–1541, Marseille, France. European Language Resources Association.
- Cite (Informal):
- The VoxWorld Platform for Multimodal Embodied Agents (Krishnaswamy et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.164.pdf
- Data
- OpenAI Gym