Generalizable Imitation Learning Through Pre-Trained Representations

• URL: http://arxiv.org/abs/2311.09350v1
• Date: Wed, 15 Nov 2023 20:15:51 GMT
• Title: Generalizable Imitation Learning Through Pre-Trained Representations
• Authors: Wei-Di Chang, Francois Hogan, David Meger, and Gregory Dudek
• Abstract summary: We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types.
• Score: 19.98418419179064
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper we leverage self-supervised vision transformer models and their emergent semantic abilities to improve the generalization abilities of imitation learning policies. We introduce BC-ViT, an imitation learning algorithm that leverages rich DINO pre-trained Visual Transformer (ViT) patch-level embeddings to obtain better generalization when learning through demonstrations. Our learner sees the world by clustering appearance features into semantic concepts, forming stable keypoints that generalize across a wide range of appearance variations and object types. We show that this representation enables generalized behaviour by evaluating imitation learning across a diverse dataset of object manipulation tasks. Our method, data and evaluation approach are made available to facilitate further study of generalization in Imitation Learners.

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