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DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models

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arxiv 2402.13181 v1 pith:SYOV6MK3 submitted 2024-02-20 cs.RO cs.LG

Norman Di Palo , Edward Johns This is my paper · ORCID
classification cs.ROcs.LG
keywords dinobotobjectnovelvisionfeaturesfoundationimage-levellearning
verification ladder T0 review T1 audit T2 compute T3 formal

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We propose DINOBot, a novel imitation learning framework for robot manipulation, which leverages the image-level and pixel-level capabilities of features extracted from Vision Transformers trained with DINO. When interacting with a novel object, DINOBot first uses these features to retrieve the most visually similar object experienced during human demonstrations, and then uses this object to align its end-effector with the novel object to enable effective interaction. Through a series of real-world experiments on everyday tasks, we show that exploiting both the image-level and pixel-level properties of vision foundation models enables unprecedented learning efficiency and generalisation. Videos and code are available at https://www.robot-learning.uk/dinobot.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robots Acquire Manipulation Skills in Seconds from a Single Human Video

    cs.RO 2026-07 conditional novelty 6.0 of 10

    A single human video is enough for a robot to acquire a new manipulation skill at inference time, with no parameter updates, reaching 62% success on 50 novel tasks.

  2. MimicFunc: Imitating Tool Manipulation from a Single Human Video via Functional Correspondence

    cs.RO 2025-08 conditional novelty 6.0 of 10

    MimicFunc transfers tool-using skills from one human video to novel tools by aligning function-centric keypoint frames, achieving 79.5% success across five manipulation tasks.

  3. CodeDiffuser: Attention-Enhanced Diffusion Policy via VLM-Generated Code for Instruction Ambiguity

    cs.RO 2025-06 conditional novelty 6.0 of 10

    CodeDiffuser uses vision-language-model-generated code to build 3D attention maps that condition a diffusion policy, improving success on ambiguous language manipulation tasks compared with end-to-end baselines.

  4. STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

    cs.RO 2024-12 conditional novelty 6.0 of 10

    Sub-trajectory retrieval with vision foundation models and dynamic time warping improves few-shot robot imitation learning over state-based and full-trajectory retrieval.

  5. GLOVER++: Unleashing the Potential of Affordance Learning from Human Behaviors for Robotic Manipulation

    cs.RO 2025-05 conditional novelty 5.0 of 10

    GLOVER++ predicts where to interact with objects from language instructions after training on the new 500K-image HOVA-500K affordance dataset, and reports gains in simulated and real manipulation.

  6. GLOVER: Generalizable Open-Vocabulary Affordance Reasoning for Task-Oriented Grasping

    cs.RO 2024-11 conditional novelty 5.0 of 10

    GLOVER predicts open-vocabulary graspable regions on objects from one RGB image and estimates grasp poses from those regions, reporting large speedups and higher success rates than prior systems.

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