Over the last decade, a variety of paradigms have sought to teach robots complex and dexterous behaviors in real-world environments. On one end of the spectrum we have nativist approaches that bake in fundamental human knowledge through physics models, simulators and knowledge graphs. While on the other end of the spectrum we have tabula-rasa approaches that teach robots from scratch. In this talk I will argue for the need for better constructivist approaches to robotics, i.e. techniques that take guidance from humans while allowing robots to continuously adapt in changing scenarios. The constructivist guide I propose will focus on three lessons. First, creating physical interfaces to allow humans to provide robots with rich and dexterous data. Second, developing adaptive learning mechanisms to allow robots to continually fine-tune in their environments. Third, architecting models that allow robots to learn from un-curated play. Applications of such a learning paradigm will be demonstrated on mobile manipulators in home environments, industrial robots on precision tasks, and multi-fingered hands on dexterous manipulation.
Bio: Lerrel Pinto is an Assistant Professor of Computer Science at NYU. His research interests focus on creating general purpose robotic systems. He received a PhD degree from CMU in 2019; prior to that he received an MS degree from CMU in 2016, and a B.Tech in Mechanical Engineering from IIT-Guwahati. His work on robotics received paper awards at ICRA 2016 and RSS 2023, and finalist awards at IROS 2019 and CoRL 2022. He is a recipient of grants and awards from Amazon, Honda, Hyundai, Meta, LG and Google. More recently, he was named a TR35 innovator under 35 for 2023. Several of his works have been featured in popular media like TechCrunch, MIT Tech Review, Wired, and BuzzFeed among others. His recent work can be found on www.lerrelpinto.com.