# One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers Source: [One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:46:58.939Z Authors: Abraham George, Amir Barati Farimani Published: 2023-09-18T21:50:26.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Stack on OneACTPlay](): Stack; OneACTPlay; Success Rate: 80. - [Pick & Place on OneACTPlay](): Pick-&-Place; OneACTPlay; Success Rate: 81.7. ## Original sources and implementation [Original paper]() [PDF]()