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Visual Pre-training for Navigation: What Can We Learn from Noise?

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arxiv 2207.00052 v3 pith:5CR5H6LA submitted 2022-06-30 cs.CV cs.AIcs.LG

Yanwei Wang , Ching-Yun Ko , Pulkit Agrawal This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords navigationviewcropcurrentdatagoalimageslearn
verification ladder T0 review T1 audit T2 compute T3 formal
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One powerful paradigm in visual navigation is to predict actions from observations directly. Training such an end-to-end system allows representations useful for downstream tasks to emerge automatically. However, the lack of inductive bias makes this system data inefficient. We hypothesize a sufficient representation of the current view and the goal view for a navigation policy can be learned by predicting the location and size of a crop of the current view that corresponds to the goal. We further show that training such random crop prediction in a self-supervised fashion purely on synthetic noise images transfers well to natural home images. The learned representation can then be bootstrapped to learn a navigation policy efficiently with little interaction data. The code is available at https://yanweiw.github.io/noise2ptz

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Cited by 3 Pith papers

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  1. Transformers Pretrained on Procedural Data Contain Modular Structures for Algorithmic Reasoning

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    A short warm-up on procedural data (brackets, sorting, sets) makes language models more accurate and more data-efficient on language, code, and informal math.

  3. Steering Robots with Inference-Time Interactions

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