O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning

• URL: http://arxiv.org/abs/2106.15087v1
• Date: Tue, 29 Jun 2021 04:38:12 GMT
• Title: O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning
• Authors: Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, Leonidas Guibas
• Abstract summary: We propose a unified affordance learning framework to learn object-object interaction for various tasks. We are able to conduct large-scale object-object affordance learning without the need for human annotations or demonstrations. Experiments on large-scale synthetic data and real-world data prove the effectiveness of the proposed approach.
• Score: 24.9242853417825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks. There is a rich space of object-object interaction scenarios in our daily life, such as placing an object on a messy tabletop, fitting an object inside a drawer, pushing an object using a tool, etc. In this paper, we propose a unified affordance learning framework to learn object-object interaction for various tasks. By constructing four object-object interaction task environments using physical simulation (SAPIEN) and thousands of ShapeNet models with rich geometric diversity, we are able to conduct large-scale object-object affordance learning without the need for human annotations or demonstrations. At the core of technical contribution, we propose an object-kernel point convolution network to reason about detailed interaction between two objects. Experiments on large-scale synthetic data and real-world data prove the effectiveness of the proposed approach. Please refer to the project webpage for code, data, video, and more materials: https://cs.stanford.edu/~kaichun/o2oafford

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