# One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers arXiv: https://arxiv.org/abs/2309.10175 · HTML: https://mlsanity.com/paper/2309.10175/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. - Builds on 19 of its 38 references that are in the map. - First posted on arXiv 18 Sep 2023. ## Placed in - Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/) ## Builds on (most cited first, 19 of 19) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017) - [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023) - [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016) - [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021) - [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018) - [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017) - [Behavior Transformers: Cloning k modes with one stone](https://mlsanity.com/paper/2206.11251/) (2022) - [Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperation](https://mlsanity.com/paper/1710.04615/) (2017) - [Reverse Curriculum Generation for Reinforcement Learning](https://mlsanity.com/paper/1707.05300/) (2017) - [RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking](https://mlsanity.com/paper/2309.01918/) (2023) - [Learning Montezuma's Revenge from a Single Demonstration](https://mlsanity.com/paper/1812.03381/) (2018) - [On the Sample Complexity of Stability Constrained Imitation Learning](https://mlsanity.com/paper/2102.09161/) (2021) - [Diffusion Model-Augmented Behavioral Cloning](https://mlsanity.com/paper/2302.13335/) (2023) - [Back to the Manifold: Recovering from Out-of-Distribution States](https://mlsanity.com/paper/2207.08673/) (2022) - [OpenVR: Teleoperation for Manipulation](https://mlsanity.com/paper/2305.09765/) (2023) - [Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning](https://mlsanity.com/paper/2209.11275/) (2022) - [RoboChop: Autonomous Framework for Fruit and Vegetable Chopping Leveraging Foundational Models](https://mlsanity.com/paper/2307.13159/) (2023) - [Creativity in Robot Manipulation with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.07459/) (2019) ## Built on by (most cited first, 17 of 17) - [Real-Time Execution of Action Chunking Flow Policies](https://mlsanity.com/paper/2506.07339/) (2025) - [Reinforcement Learning with Action Chunking](https://mlsanity.com/paper/2507.07969/) (2025) - [Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling](https://mlsanity.com/paper/2408.17355/) (2024) - [Bi-ACT: Bilateral Control-Based Imitation Learning via Action Chunking with Transformer](https://mlsanity.com/paper/2401.17698/) (2024) - [VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies](https://mlsanity.com/paper/2403.11898/) (2024) - [SculptDiff: Learning Robotic Clay Sculpting from Humans with Goal Conditioned Diffusion Policy](https://mlsanity.com/paper/2403.10401/) (2024) - [RT-Cache: Training-Free Retrieval for Real-Time Manipulation](https://mlsanity.com/paper/2505.09040/) (2025) - [StreamingVLA: Streaming Vision-Language-Action Model with Action Flow Matching and Adaptive Early Observation](https://mlsanity.com/paper/2603.28565/) (2026) - [ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning](https://mlsanity.com/paper/2410.15994/) (2024) - [Closed-Loop Action Chunks with Dynamic Corrections for Training-Free Diffusion Policy](https://mlsanity.com/paper/2603.01953/) (2026) - [Subconscious Robotic Imitation Learning](https://mlsanity.com/paper/2412.20368/) (2024) - [ALPHA-α and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System](https://mlsanity.com/paper/2411.09942/) (2024) - [What Do You Need for Compositional Generalization in Diffusion Planning?](https://mlsanity.com/paper/2505.18083/) (2025) - [One-shot Adaptation of Humanoid Whole-body Motion with Walking Priors](https://mlsanity.com/paper/2510.25241/) (2025) - [Expert Behavior Prior Reinforcement Learning](https://mlsanity.com/paper/2607.21302/) (2026) - [Improving Online Reinforcement Learning via Bidirectional Behavior Prior Distillation](https://mlsanity.com/paper/2609.20268/) (2026) - [REACT: Rolling Denoising and Dual Decoupling for Reactive Robot Control with VLA Models](https://mlsanity.com/paper/2610.12007/) (2026) ## Abstract (arXiv) 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.