# MILES: Making Imitation Learning Easy with Self-Supervision

arXiv: https://arxiv.org/abs/2410.19693 · HTML: https://mlsanity.com/paper/2410.19693/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 37 of its 44 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Builds on (most cited first, 30 of 37)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [RT-1: Robotics Transformer for Real-World Control at Scale](https://mlsanity.com/paper/2212.06817/) (2022)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware](https://mlsanity.com/paper/2304.13705/) (2023)
- [What Matters in Learning from Offline Human Demonstrations for Robot Manipulation](https://mlsanity.com/paper/2108.03298/) (2021)
- [RLBench: The Robot Learning Benchmark & Learning Environment](https://mlsanity.com/paper/1909.12271/) (2019)
- [BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning](https://mlsanity.com/paper/2202.02005/) (2022)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations](https://mlsanity.com/paper/2310.17596/) (2023)
- [One-Shot Visual Imitation Learning via Meta-Learning](https://mlsanity.com/paper/1709.04905/) (2017)
- [DART: Noise Injection for Robust Imitation Learning](https://mlsanity.com/paper/1703.09327/) (2017)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning](https://mlsanity.com/paper/2401.16013/) (2024)
- [Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration](https://mlsanity.com/paper/2105.06411/) (2021)
- [You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration](https://mlsanity.com/paper/2201.12716/) (2022)
- [Provably Efficient Imitation Learning from Observation Alone](https://mlsanity.com/paper/1905.10948/) (2019)
- [Teach a Robot to FISH: Versatile Imitation from One Minute of Demonstrations](https://mlsanity.com/paper/2303.01497/) (2023)
- [Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards](https://mlsanity.com/paper/1906.05841/) (2019)
- [NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis](https://mlsanity.com/paper/2301.08556/) (2023)
- [ToolFlowNet: Robotic Manipulation with Tools via Predicting Tool Flow from Point Clouds](https://mlsanity.com/paper/2211.09006/) (2022)
- [Towards More Generalizable One-shot Visual Imitation Learning](https://mlsanity.com/paper/2110.13423/) (2021)
- [Offline Meta-Reinforcement Learning for Industrial Insertion](https://mlsanity.com/paper/2110.04276/) (2021)
- [Grasping with Chopsticks: Combating Covariate Shift in Model-free Imitation Learning for Fine Manipulation](https://mlsanity.com/paper/2011.06719/) (2020)
- [Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning](https://mlsanity.com/paper/2204.02863/) (2022)
- [R+X: Retrieval and Execution from Everyday Human Videos](https://mlsanity.com/paper/2407.12957/) (2024)
- [Imitation Learning with Sinkhorn Distances](https://mlsanity.com/paper/2008.09167/) (2020)
- [Robust Multi-Modal Policies for Industrial Assembly via Reinforcement Learning and Demonstrations: A Large-Scale Study](https://mlsanity.com/paper/2103.11512/) (2021)

## Built on by (most cited first, 10 of 10)

- [One-Shot Dual-Arm Imitation Learning](https://mlsanity.com/paper/2503.06831/) (2025)
- [Weakly-Supervised Learning of Dense Functional Correspondences](https://mlsanity.com/paper/2509.03893/) (2025)
- [Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)](https://mlsanity.com/paper/2509.06191/) (2025)
- [Self-Augmented Robot Trajectory: Efficient Imitation Learning via Safe Self-augmentation with Demonstrator-annotated Precision](https://mlsanity.com/paper/2509.09893/) (2025)
- [FoldNet: Learning Generalizable Closed-Loop Policy for Garment Folding via Keypoint-Driven Asset and Demonstration Synthesis](https://mlsanity.com/paper/2505.09109/) (2025)
- [One Demonstration Is Enough for Real-World Robotic Reinforcement Learning](https://mlsanity.com/paper/2607.01651/) (2026)
- [Learning Generalizable 3D Manipulation With 10 Demonstrations](https://mlsanity.com/paper/2411.10203/) (2024)
- [SAGE: Scene Graph-Aware Guidance and Execution for Long-Horizon Manipulation Tasks](https://mlsanity.com/paper/2509.21928/) (2025)
- [UMI-Underwater: Learning Underwater Manipulation without Underwater Teleoperation](https://mlsanity.com/paper/2603.27012/) (2026)
- [MinInter: Minimizing Trajectory Interpolation During Data Augmentation for Imitation Learning](https://mlsanity.com/paper/2606.24078/) (2026)

## Abstract (arXiv)

Data collection in imitation learning often requires significant, laborious human supervision, such as numerous demonstrations, and/or frequent environment resets for methods that incorporate reinforcement learning. In this work, we propose an alternative approach, MILES: a fully autonomous, self-supervised data collection paradigm, and we show that this enables efficient policy learning from just a single demonstration and a single environment reset. MILES autonomously learns a policy for returning to and then following the single demonstration, whilst being self-guided during data collection, eliminating the need for additional human interventions. We evaluated MILES across several real-world tasks, including tasks that require precise contact-rich manipulation such as locking a lock with a key. We found that, under the constraints of a single demonstration and no repeated environment resetting, MILES significantly outperforms state-of-the-art alternatives like imitation learning methods that leverage reinforcement learning. Videos of our experiments and code can be found on our webpage: www.robot-learning.uk/miles.