DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models
원문 초록
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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What Matters in Learning from Large-Scale Datasets for Robot Manipulation
EC-Flow: Enabling Versatile Robotic Manipulation from Action-Unlabeled Videos via Embodiment-Centric Flow
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval
SAMURAI: Shape-Aware Multimodal Retrieval for 3D Object Identification
Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
DetFusion: A Detection-driven Infrared and Visible Image Fusion Network
토론
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