Multi-View Masked World Models for Visual Robotic Manipulation

• URL: http://arxiv.org/abs/2302.02408v2
• Date: Wed, 31 May 2023 08:13:44 GMT
• Title: Multi-View Masked World Models for Visual Robotic Manipulation
• Authors: Younggyo Seo, Junsu Kim, Stephen James, Kimin Lee, Jinwoo Shin, Pieter Abbeel
• Abstract summary: We train a multi-view masked autoencoder which reconstructs pixels of randomly masked viewpoints. We demonstrate the effectiveness of our method in a range of scenarios. We also show that the multi-view masked autoencoder trained with multiple randomized viewpoints enables training a policy with strong viewpoint randomization.
• Score: 132.97980128530017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual robotic manipulation research and applications often use multiple cameras, or views, to better perceive the world. How else can we utilize the richness of multi-view data? In this paper, we investigate how to learn good representations with multi-view data and utilize them for visual robotic manipulation. Specifically, we train a multi-view masked autoencoder which reconstructs pixels of randomly masked viewpoints and then learn a world model operating on the representations from the autoencoder. We demonstrate the effectiveness of our method in a range of scenarios, including multi-view control and single-view control with auxiliary cameras for representation learning. We also show that the multi-view masked autoencoder trained with multiple randomized viewpoints enables training a policy with strong viewpoint randomization and transferring the policy to solve real-robot tasks without camera calibration and an adaptation procedure. Video demonstrations are available at: https://sites.google.com/view/mv-mwm.

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