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Large offline learning-based models have enabled robots to successfullyinteract with objects for a wide variety of tasks. However, these models relyon fairly consistent structured environments. For more unstructuredenvironments, an online learning component is necessary to gather and estimateinformation about objects in the environment in order to successfully interactwith them. Unfortunately, online learning methods like Bayesian non-parametricmodels struggle with changes in the environment, which is often the desiredoutcome of interaction-based tasks. We propose using an object-centricrepresentation for interactive online learning. This representation isgenerated by transforming the robot's actions into the object's coordinateframe. We demonstrate how switching to this task-relevant space improves ourability to reason with the training data collected online, enabling scalableonline learning of robot-object interactions. We showcase our method bysuccessfully navigating a manipulator arm through an environment with multipleunknown objects without violating interaction-based constraints.

Object-centric Representations for Interactive Online Learning with Non-Parametric Methods