# On the Effectiveness of Retrieval, Alignment, and Replay in Manipulation

arXiv: https://arxiv.org/abs/2312.12345 · HTML: https://mlsanity.com/paper/2312.12345/

## Key facts

- Cited by 15 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 17 of its 18 references that are in the map.
- First posted on arXiv 19 Dec 2023.

## 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, 17 of 17)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [R3M: A Universal Visual Representation for Robot Manipulation](https://mlsanity.com/paper/2203.12601/) (2022)
- [Deep ViT Features as Dense Visual Descriptors](https://mlsanity.com/paper/2112.05814/) (2021)
- [The Surprising Effectiveness of Representation Learning for Visual Imitation](https://mlsanity.com/paper/2112.01511/) (2021)
- [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)
- [Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets](https://mlsanity.com/paper/2304.08742/) (2023)
- [Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning](https://mlsanity.com/paper/2204.02863/) (2022)
- [Affordance Learning from Play for Sample-Efficient Policy Learning](https://mlsanity.com/paper/2203.00352/) (2022)
- [Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning](https://mlsanity.com/paper/2005.10872/) (2020)
- [Learning Multi-Stage Tasks with One Demonstration via Self-Replay](https://mlsanity.com/paper/2111.07447/) (2021)
- [One-Shot Imitation Learning: A Pose Estimation Perspective](https://mlsanity.com/paper/2310.12077/) (2023)
- [Few-Shot In-Context Imitation Learning via Implicit Graph Alignment](https://mlsanity.com/paper/2310.12238/) (2023)
- [Where To Start? Transferring Simple Skills to Complex Environments](https://mlsanity.com/paper/2212.06111/) (2022)

## Built on by (most cited first, 15 of 15)

- [DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models](https://mlsanity.com/paper/2402.13181/) (2024)
- [R+X: Retrieval and Execution from Everyday Human Videos](https://mlsanity.com/paper/2407.12957/) (2024)
- [Learning Precise Affordances from Egocentric Videos for Robotic Manipulation](https://mlsanity.com/paper/2408.10123/) (2024)
- [GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy](https://mlsanity.com/paper/2410.17488/) (2024)
- [Offline Imitation Learning Through Graph Search and Retrieval](https://mlsanity.com/paper/2407.15403/) (2024)
- [Guiding Data Collection via Factored Scaling Curves](https://mlsanity.com/paper/2505.07728/) (2025)
- [MILES: Making Imitation Learning Easy with Self-Supervision](https://mlsanity.com/paper/2410.19693/) (2024)
- [Learning Part-Aware Dense 3D Feature Field for Generalizable Articulated Object Manipulation](https://mlsanity.com/paper/2602.14193/) (2026)
- [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)
- [ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models](https://mlsanity.com/paper/2408.15919/) (2024)
- [ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstration](https://mlsanity.com/paper/2606.09381/) (2026)
- [Retrieve in Time, Correct in Frequency](https://mlsanity.com/paper/2608.04527/) (2026)
- [Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist](https://mlsanity.com/paper/2601.19275/) (2026)
- [RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modes](https://mlsanity.com/paper/2606.24403/) (2026)

## Abstract (arXiv)

Imitation learning with visual observations is notoriously inefficient when addressed with end-to-end behavioural cloning methods. In this paper, we explore an alternative paradigm which decomposes reasoning into three phases. First, a retrieval phase, which informs the robot what it can do with an object. Second, an alignment phase, which informs the robot where to interact with the object. And third, a replay phase, which informs the robot how to interact with the object. Through a series of real-world experiments on everyday tasks, such as grasping, pouring, and inserting objects, we show that this decomposition brings unprecedented learning efficiency, and effective inter- and intra-class generalisation. Videos are available at https://www.robot-learning.uk/retrieval-alignment-replay.