Dorsa Sadigh @ InterNLP 2021
Channel: InterNLP 2021 
Date: 15/09/2021

Beyond Imitation Learning: New Paradigms of Querying and Integrating Different Types of Human Data for Improved Robot Learning

Learning from humans is a fundamental component of building better robotic algorithms that can achieve complex tasks. This plays a central role in specifying how dynamical systems should act, i.e., how an assistive robot arm should move, or how an autonomous car should drive. Learning such models is highly dependent on access to a large amount of expert data collected from humans interacting with robots. However, we usually do not have access to such a large dataset in robotics settings, and even the data provided can be noisy, inaccurate, repetitive, or not informative. In this talk, we will discuss two core ideas to effectively learn from interactions with humans. First, we discuss how to develop active learning techniques that query humans in a directed manner in order to learn from them. Second, we discuss how we can tap into many different sources of data beyond expert demonstrations to learn in an interactive fashion from humans. We discuss some of our recent work on learning from physical corrections, preferences, and suboptimal human data. We end the talk by discussing a new thread of work on integrating language in the robot learning loop to enable language-enabled shared autonomy systems for assistive teleoperation.