Title: Learning Instance Segmentation by Interaction,
Abstract: We present an approach for building an active agent that learns to segment
its visual observations into individual objects by interacting with its
environment in a completely self-supervised manner. The agent uses its current
segmentation model to infer pixels that constitute objects and refines the
segmentation model by interacting with these pixels. The model learned from
over 50K interactions generalizes to novel objects and backgrounds. To deal
with noisy training signal for segmenting objects obtained by self-supervised
interactions, we propose robust set loss. A dataset of robot's interactions
along-with a few human labeled examples is provided as a benchmark for future
research. We test the utility of the learned segmentation model by providing
results on a downstream vision-based control task of rearranging multiple
objects into target configurations from visual inputs alone. Videos, code, and
robotic interaction dataset are available at
this https URL