Mediating Shared Basis in Situated Human Robot Dialogue
Supported by National Science Foundation (10/1/2012 - Present), in collaboration with Professor Ning Xi (Co-PI) and The Robotics and Automation Laboratory at MSU.
Humans and robots have different representations of the shared environment. They also have different capabilities to attend and respond to the unexpected events in the environment, To support language based communication, humans and robots need to establish a shared basis and jointly handle exceptions. To address this issue, this work intends to develop a novel framework that tightly integrates high level language and dialogue processing with low level sensing and control systems and supports collaborative dialogue to mediate a shared basis between humans and robots.
The following video demonstrates the use of graph-based approach to keep track of collaborative dialogue discourse and to mediate shared perceptual basis between humans and robots.
The following video demonstrates that during nautral language interaction, exceptions occur and exception handling is seamlessly integrated with the lower-level control system.
The following video demonstrates teaching robots new actions through natural language dialogue.
- Embodied Collaborative Referring Expression Generation in Situated Human-Robot Dialogue. R. Fang, M. Doering, and J. Y. Chai. Proceedings of the 10th ACM/IEEE Conference on Human-Robot Interaction (HRI), Portland, Oregon, March 2-5, 2015.
- Learning to Mediate Perceptual Differences in Situated Human-Robot Dialogue. C. Liu and J. Y. Chai. Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI), Austin, Texas, January 25-30, 2015.
- Collaborative Models for Referring Expression Generation towards Situated Dialogue. R. Fang, M. Doering, and J. Y. Chai. Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), Quebec, Canada, August, 2014.
- Probabilistic Labeling for Efficient Referential Grounding based on Collaborative Discourse. C. Liu, L. She, R. Fang, and J. Y. Chai. Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics (ACL), Short Paper, Baltimore, MD, June 22-26, 2014.
- Teaching Robots New Actions through Natural Language Instructions. L. She, Y. Cheng, J. Y. Chai, Y. Jia, S. Yang, and N. Xi. The 23rd IEEE International Symposium on Robot and Human Interactive Communication, Edinburgh, UK, August 25-29,2014.
- Back to the Blocks World: Learning New Actions through Situated Human-Robot Dialogue. L. She, S. Yang, Y. Cheng, Y. Jia, J. Y. Chai, and N. Xi. Proceedings of 15th SIGDIAL Meeting on Discourse and Dialogue, Philadelphia, PA, June 18-20, 2014.
- Perceptive Feedback for Natural Language Control of Robotic Operations. Y. Jia, N. Xi, J. Y. Chai, Y. Cheng, R. Fang, and L. She. 2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China, May 31 - June 7, 2014.
- Collaborative Effort towards Common Ground in Situated Human-Robot Dialogue. J. Y. Chai, L. She, R. Fang, S. Ottarson, C. Littley, C. Liu, and K. Hanson. The 9th ACM/IEEE Conference on Human-Robot Interaction (HRI), Bielefeld, Germany, March 3-6, 2014.
- Fang, C. Liu, L. She, and J. Y. Chai. Towards Situated Dialogue: Revisiting Referring Expression Generation. Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, Washington, October, 2013.
- C. Liu, R. Fang, L. She, and J.Y. Chai. Modeling Collaborative Referring for Situated Referential Grounding. The 14th Annual SIGDIAL Meeting on Discourse and Dialogue (SIGDIAL), pp. 78-86, Metz, France, August, 2013.