  Robots can use Visual Imitation Learning (VIL) to learn everyday tasks from
video demonstrations. However, translating visual observations into actionable
robot policies is challenging due to the high-dimensional nature of video data.
This challenge is further exacerbated by the morphological differences between
humans and robots, especially when the video demonstrations feature humans
performing tasks. To address these problems we introduce Visual Imitation
lEarning with Waypoints (VIEW), an algorithm that significantly enhances the
sample efficiency of human-to-robot VIL. VIEW achieves this efficiency using a
multi-pronged approach: extracting a condensed prior trajectory that captures
the demonstrator's intent, employing an agent-agnostic reward function for
feedback on the robot's actions, and utilizing an exploration algorithm that
efficiently samples around waypoints in the extracted trajectory. VIEW also
segments the human trajectory into grasp and task phases to further accelerate
learning efficiency. Through comprehensive simulations and real-world
experiments, VIEW demonstrates improved performance compared to current
state-of-the-art VIL methods. VIEW enables robots to learn a diverse range of
manipulation tasks involving multiple objects from arbitrarily long video
demonstrations. Additionally, it can learn standard manipulation tasks such as
pushing or moving objects from a single video demonstration in under 30
minutes, with fewer than 20 real-world rollouts. Code and videos here:
https://collab.me.vt.edu/view/
