When a robot is deployed to a new setting, it must reason about how to accomplish the goals of domain-appropriate tasks within the environment it is situated. We investigate the problem of enabling robots to interactively learn how to perform known tasks in new environments. Each task is composed of a sequence of parameterized actions, which we assume are given to the robot in the form of a task recipe. In order to learn how to ground the task in a new environment, our learner builds classifiers to model each of the parameters (i.e. all unique objects and semantic locations) associated with the task. In evaluation for two tasks across three different environments, our results show that these groundings are both (1) capable of being learned efficiently from demonstrations, and (2) necessary to learn for each new environment.
['Kalesha Bullard', 'Baris Akgun', 'Sonia Chernova', 'Andrea Lockerd Thomaz']
Grounding action parameters from demonstration