mlsanityData to 14 Oct 2026

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

arXiv 2312.12345 ↗First posted 19 Dec 2023cs.RO cs.LGcited by 15

On the map: Offline RL and imitation learning from data → Robot manipulation imitation learning

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.

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.

Builds on

17 of 18 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023Few-Shot In-Context Imitation Learning via Implicit Graph Alignment18 Oct
  2. 2023One-Shot Imitation Learning: A Pose Estimation Perspective18 Oct
  3. 2023Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets18 Apr
  4. 2022Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning6 Apr
  5. 2021The Surprising Effectiveness of Representation Learning for Visual Imitation2 Dec
  6. 2021Learning Multi-Stage Tasks with One Demonstration via Self-Replay14 Nov
  7. 2021Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstration13 May

Other references · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,968
  5. 2022R3M: A Universal Visual Representation for Robot Manipulationcited by 694
  6. 2021Deep ViT Features as Dense Visual Descriptorscited by 330
  7. 2022You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstrationcited by 81
  8. 2022Affordance Learning from Play for Sample-Efficient Policy Learningcited by 30
  9. 2020Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learningcited by 25
  10. 2022Where To Start? Transferring Simple Skills to Complex Environmentscited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 7; 2025: 3; 2026: 5.

15 papers.

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  1. 2024DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Modelscited by 44
  2. 2024R+X: Retrieval and Execution from Everyday Human Videoscited by 38
  3. 2024Learning Precise Affordances from Egocentric Videos for Robotic Manipulationcited by 34
  4. 2024GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policycited by 25
  5. 2024Offline Imitation Learning Through Graph Search and Retrievalcited by 14
  6. 2025Guiding Data Collection via Factored Scaling Curvescited by 12
  7. 2024MILES: Making Imitation Learning Easy with Self-Supervisioncited by 10
  8. 2026Learning Part-Aware Dense 3D Feature Field for Generalizable Articulated Object Manipulationcited by 5
  9. 2025Weakly-Supervised Learning of Dense Functional Correspondencescited by 2
  10. 2025Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)cited by 2
  11. 2024ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Modelscited by 1
  12. 2026ReGIL: Retrieval-Guided Imitation Learning from a Single Demonstrationcited by 1
3 more
  1. 2026Retrieve in Time, Correct in Frequencycited by 1
  2. 2026Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wristcited by 0
  3. 2026RE4: Transformation-aware Imitation of Object Interactions Using Manipulation Modescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Learning a Thousand Tasks in a Day
  2. 2022Learning and Retrieval from Prior Data for Skill-based Imitation Learning
  3. 2026Difference-Aware Retrieval Policies for Imitation Learning
  4. 2026Human-to-Robot Interaction: Learning from Video Demonstration for Robot Imitation
  5. 2025Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning
  6. 2025A Smooth Sea Never Made a Skilled SAILOR: Robust Imitation via Learning to Search
  7. 2026Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation
  8. 2025Robotic Manipulation via Imitation Learning: Taxonomy, Evolution, Benchmark, and Challenges