  When robots operate in human environments, it's critical that humans can
quickly teach them new concepts: object-centric properties of the environment
that they care about (e.g. objects near, upright, etc). However, teaching a new
perceptual concept from high-dimensional robot sensor data (e.g. point clouds)
is demanding, requiring an unrealistic amount of human labels. To address this,
we propose a framework called Perceptual Concept Bootstrapping (PCB). First, we
leverage the inherently lower-dimensional privileged information, e.g., object
poses and bounding boxes, available from a simulator only at training time to
rapidly learn a low-dimensional, geometric concept from minimal human input.
Second, we treat this low-dimensional concept as an automatic labeler to
synthesize a large-scale high-dimensional data set with the simulator. With
these two key ideas, PCB alleviates human label burden while still learning
perceptual concepts that work with real sensor input where no privileged
information is available. We evaluate PCB for learning spatial concepts that
describe object state or multi-object relationships, and show it achieves
superior performance compared to baseline methods. We also demonstrate the
utility of the learned concepts in motion planning tasks on a 7-DoF Franka
Panda robot.
