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Nakul Gopalan
Nakul Gopalan
Learning Transferable Symbols and Language Groundings from Perceptual Data for Instruction Following
Learning Transferable Symbols and Language Groundings from Perceptual Data for Instruction Following
A collaborative robot should be able to learn novel task specifications from its users to be a general purpose, programmable device. To learn novel tasks from people we must enable robots to learn 1) knowledge representations that can be leveraged for efficient planning and skill learning and; 2) mechanisms for natural language communication that enable the robot to understand a human partner's intent. In this work, I solve both of these problems. I show how representations for planning and language grounding can be learned together to follow commands in novel environments. This approach provides a framework to teach robots unstructured tasks via language to enable deployment of cooperative robots in homes, offices and industries.
Biography
Biography
Nakul Gopalan is a postdoctoral researcher in the CORE Robotics Lab with Prof. Matthew Gombolay at Georgia Tech. He completed his PhD at Brown University's Computer Science department in 2019. Previously he was a graduate student in Prof. Stefanie Tellex's H2R lab at Brown. His research interests lie at the intersection of language grounding and robot learning. Nakul has developed algorithms and methods that allow robots to be trained by leveraging demonstrations and natural language descriptions. Such learning would improve the usability of robots within homes and offices. His other research interests are in hierarchical reinforcement learning and planning. His work has received a best paper award at the RoboNLP workshop at ACL 2017.
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