One ACT Play: Single Demonstration Behavior Cloning with Action Chunking
  Transformers
Abraham George Amir Barati Farimani
Learning from human demonstrations (behavior cloning) is a cornerstone of
robot learning. However, most behavior cloning algorithms require a large
number of demonstrations to learn a task, especially for general tasks that
have a large variety of initial conditions. Humans, however, can learn to
complete tasks, even complex ones, after only seeing one or two demonstrations.
Our work seeks to emulate this ability, using behavior cloning to learn a task
given only a single human demonstration. We achieve this goal by using linear
transforms to augment the single demonstration, generating a set of
trajectories for a wide range of initial conditions. With these demonstrations,
we are able to train a behavior cloning agent to successfully complete three
block manipulation tasks. Additionally, we developed a novel addition to the
temporal ensembling method used by action chunking agents during inference. By
incorporating the standard deviation of the action predictions into the
ensembling method, our approach is more robust to unforeseen changes in the
environment, resulting in significant performance improvements.