  Visual imitation learning provides a framework for learning complex
manipulation behaviors by leveraging human demonstrations. However, current
interfaces for imitation such as kinesthetic teaching or teleoperation
prohibitively restrict our ability to efficiently collect large-scale data in
the wild. Obtaining such diverse demonstration data is paramount for the
generalization of learned skills to novel scenarios. In this work, we present
an alternate interface for imitation that simplifies the data collection
process while allowing for easy transfer to robots. We use commercially
available reacher-grabber assistive tools both as a data collection device and
as the robot's end-effector. To extract action information from these visual
demonstrations, we use off-the-shelf Structure from Motion (SfM) techniques in
addition to training a finger detection network. We experimentally evaluate on
two challenging tasks: non-prehensile pushing and prehensile stacking, with
1000 diverse demonstrations for each task. For both tasks, we use standard
behavior cloning to learn executable policies from the previously collected
offline demonstrations. To improve learning performance, we employ a variety of
data augmentations and provide an extensive analysis of its effects. Finally,
we demonstrate the utility of our interface by evaluating on real robotic
scenarios with previously unseen objects and achieve a 87% success rate on
pushing and a 62% success rate on stacking. Robot videos are available at
https://dhiraj100892.github.io/Visual-Imitation-Made-Easy.
