Abstract: Robots are increasingly present in our lives, from cleaning our houses to automating logistics. However, these robots are still present in our lives as solitary agents, performing structured tasks, without the power to collaborate and learn with humans.
A key challenge here is that robots perceive the world and operate in it using sensors and actuators that are continuous, low-level and noisy. However, people on the other hand, reason, plan, specify and teach tasks using high-level concepts without worrying about the low-level continuous nature of the world. To address this challenge, I develop computational methods that firstly, allow robots to learn representations, and skills to solve novel tasks. Moreover, these methods and representations also enable robots to be taught and programmed using natural language communication, allowing robots to understand a human partner's intent.
In this talk, I first demonstrate how representations for planning and language understanding can be learned together to follow commands in novel environments. In the second part of the talk, I demonstrate a more practical approach in which language can be grounded to pre-trained deep policy representations to solve novel task specifications.
Together, these approaches empower robots to learn unstructured tasks via language and demonstrations. I will then discuss the implications of such approaches in collaborative task solving with robots in homes, offices, and industries.
Bio: 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 ACL2017.
This seminar will be streaming via Zoom at the link above but requires a passcode. To obtain the passcode for this event, please see the announcements on the college email lists or contact us.