  Large offline learning-based models have enabled robots to successfully
interact with objects for a wide variety of tasks. However, these models rely
on fairly consistent structured environments. For more unstructured
environments, an online learning component is necessary to gather and estimate
information about objects in the environment in order to successfully interact
with them. Unfortunately, online learning methods like Bayesian non-parametric
models struggle with changes in the environment, which is often the desired
outcome of interaction-based tasks. We propose using an object-centric
representation for interactive online learning. This representation is
generated by transforming the robot's actions into the object's coordinate
frame. We demonstrate how switching to this task-relevant space improves our
ability to reason with the training data collected online, enabling scalable
online learning of robot-object interactions. We showcase our method by
successfully navigating a manipulator arm through an environment with multiple
unknown objects without violating interaction-based constraints.
