# ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration

arXiv: https://arxiv.org/abs/2606.09381 · HTML: https://mlsanity.com/paper/2606.09381/

## Key facts

- Cited by 1 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 29 of its 35 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## 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, 29 of 29)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [A Minimalist Approach to Offline Reinforcement Learning](https://mlsanity.com/paper/2106.06860/) (2021)
- [Behavioral Cloning from Observation](https://mlsanity.com/paper/1805.01954/) (2018)
- [Efficient Online Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2302.02948/) (2023)
- [Overcoming Exploration in Reinforcement Learning with Demonstrations](https://mlsanity.com/paper/1709.10089/) (2017)
- [Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning](https://mlsanity.com/paper/2410.21845/) (2024)
- [OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning](https://mlsanity.com/paper/2010.13611/) (2020)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms](https://mlsanity.com/paper/1907.03146/) (2019)
- [Primal Wasserstein Imitation Learning](https://mlsanity.com/paper/2006.04678/) (2020)
- [Jump-Start Reinforcement Learning](https://mlsanity.com/paper/2204.02372/) (2022)
- [BAKU: An Efficient Transformer for Multi-Task Policy Learning](https://mlsanity.com/paper/2406.07539/) (2024)
- [Watch and Match: Supercharging Imitation with Regularized Optimal Transport](https://mlsanity.com/paper/2206.15469/) (2022)
- [Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets](https://mlsanity.com/paper/2304.08742/) (2023)
- [RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulation](https://mlsanity.com/paper/2407.04689/) (2024)
- [Cross-Domain Imitation Learning via Optimal Transport](https://mlsanity.com/paper/2110.03684/) (2021)
- [DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models](https://mlsanity.com/paper/2402.13181/) (2024)
- [STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning](https://mlsanity.com/paper/2412.15182/) (2024)
- [Meta-World+: An Improved, Standardized, RL Benchmark](https://mlsanity.com/paper/2505.11289/) (2025)
- [Optimal Transport for Offline Imitation Learning](https://mlsanity.com/paper/2303.13971/) (2023)
- [Imitation Learning with Sinkhorn Distances](https://mlsanity.com/paper/2008.09167/) (2020)
- [Aligning Time Series on Incomparable Spaces](https://mlsanity.com/paper/2006.12648/) (2020)
- [On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation](https://mlsanity.com/paper/2312.12345/) (2023)
- [Offline Imitation Learning Through Graph Search and Retrieval](https://mlsanity.com/paper/2407.15403/) (2024)
- [Zero-shot Imitation Policy via Search in Demonstration Dataset](https://mlsanity.com/paper/2401.16398/) (2024)
- [HAND Me the Data: Fast Robot Adaptation via Hand Path Retrieval](https://mlsanity.com/paper/2505.20455/) (2025)
- [Robot Policy Learning with Temporal Optimal Transport Reward](https://mlsanity.com/paper/2410.21795/) (2024)

## Built on by (most cited first, 1 of 1)

- [Retrieve in Time, Correct in Frequency](https://mlsanity.com/paper/2608.04527/) (2026)

## Abstract (arXiv)

Learning robot manipulation policies with deep neural networks from a single demonstration remains highly challenging, as even small deviations from the demonstrated trajectory can quickly compound into failure, while collecting substantial online interaction data is costly. We propose ReGIL, a retrieval-guided imitation learning framework that treats a single demonstration as an external memory. ReGIL repeatedly queries this static memory throughout training to simultaneously guide exploration, generate the regularization buffer, and construct rewards. Specifically, it computes rewards through local temporal alignment between the current trajectory and the retrieved segment, providing step-wise and informative feedback for policy improvement. We evaluate ReGIL on robotic manipulation tasks from the LIBERO and Meta-World benchmarks under the single demonstration setting. ReGIL outperforms prior baselines in both success rate and training efficiency. In real-robot experiments, using only one demonstration and less than one hour of online training, ReGIL achieves over 75% success rate across three manipulation tasks with randomness in both initial robot pose and target position. These results demonstrate that leveraging the single demonstration as reusable memory can provide more than static supervision for efficient robot learning. More details can be found on our website: https://regil2026.github.io/