  The study of object representations in computer vision has primarily focused
on developing representations that are useful for image classification, object
detection, or semantic segmentation as downstream tasks. In this work we aim to
learn object representations that are useful for control and reinforcement
learning (RL). To this end, we introduce Transporter, a neural network
architecture for discovering concise geometric object representations in terms
of keypoints or image-space coordinates. Our method learns from raw video
frames in a fully unsupervised manner, by transporting learnt image features
between video frames using a keypoint bottleneck. The discovered keypoints
track objects and object parts across long time-horizons more accurately than
recent similar methods. Furthermore, consistent long-term tracking enables two
notable results in control domains -- (1) using the keypoint co-ordinates and
corresponding image features as inputs enables highly sample-efficient
reinforcement learning; (2) learning to explore by controlling keypoint
locations drastically reduces the search space, enabling deep exploration
(leading to states unreachable through random action exploration) without any
extrinsic rewards.
