Aishwarya Padmakumar
Ph.D. Alumni
I am a recent PhD graduate from the Department of Computer Science at the University of Texas at Austin. My research with Prof. Ray Mooney is directed towards enabling lifelong learning in grounded language understanding systems, with a focus on human-robot dialog applications.
Homepage:
http://www.cs.utexas.edu/~aish
Publications
     [Expand to show all 12][Minimize]
Dialog Policy Learning for Joint Clarification and Active Learning Queries 2021
Aishwarya Padmakumar, Raymond J. Mooney, In The AAAI Conference on Artificial Intelligence (AAAI), Vol. , February 2021.
Dialog as a Vehicle for Lifelong Learning 2020
Aishwarya Padmakumar, Raymond J. Mooney, In Position Paper Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDial 2.0), July 2020.
Dialog as a Vehicle for Lifelong Learning of Grounded Language Understanding Systems 2020
Aishwarya Padmakumar, PhD Thesis, Department of Computer Science, The University of Texas at Austin.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog 2020
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, Raymond J. Mooney, The Journal of Artificial Intelligence Research (JAIR), Vol. 67 (2020), pp. 327-374.
Improving Grounded Natural Language Understanding through Human-Robot Dialog 2019
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 2019.
Improved Models and Queries for Grounded Human-Robot Dialog 2018
Aishwarya Padmakumar, PhD Proposal, Department of Computer Science, The University of Texas At Austin.
Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence 2018
Justin Hart, Harel Yedidsion, Yuqian Jiang, Nick Walker, Rishi Shah, Jesse Thomason, Aishwarya Padmakumar, Rolando Fernandez, Jivko Sinapov, Raymond Mooney, Peter Stone, In Artificial Intelligence (AI) for Human-Robot Interaction (HRI) symposium, AAAI Fall Symposium Series, Arlington, Virginia, October 2018.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog 2018
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In RSS Workshop on Models and Representations for Natural Human-Robot Communication (MRHRC-18). Robotics: Science and Systems (RSS), June 2018.
Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog 2018
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney, In Late-breaking Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDIAL-18), Melbourne, Australia, July 2018.
Learning a Policy for Opportunistic Active Learning 2018
Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney, In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP-18), Brussels, Belgium, November 2018.
Integrated Learning of Dialog Strategies and Semantic Parsing 2017
Aishwarya Padmakumar, Jesse Thomason, and Raymond J. Mooney, In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), pp. 547--557, Valencia, Spain, April 2017.
Opportunistic Active Learning for Grounding Natural Language Descriptions 2017
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Justin Hart, Peter Stone, and Raymond J. Mooney, In Proceedings of the 1st Annual Conference on Robot Learning (CoRL-17), Sergey Levine and Vincent Vanhoucke and Ken Goldberg (Eds.), pp. 67--76, Mountain View, California, November 2017. PMLR.
Areas of Interest
Connecting Language and Perception Language and Robotics Learning for Recommender Systems Machine Learning Natural Language Processing Reinforcement Learning
Labs
Formerly affiliated with Machine Learning