Collision Risk Prediction and Visualization Based on Transformer PonNet in Object Placement Tasks by Domestic Service Robots

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  • 生活支援ロボットによる物体配置タスクにおけるTransformer PonNetに基づく危険性予測および可視化

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<p>Placing everyday objects in designated areas, such as placing a glass on a table, is a crucial task for Domestic service robots (DSRs). In this paper, we propose a physical reasoning method about collisions in placement tasks. The proposed method, Transformer PonNet, predicts the probability of a possible collision and visualizes areas involved in the collision. Unlike existing methods, Transformer PonNet can be applied to objects whose models are unavailable. We propose a novel Transformer Perception Branch that handles relationships among features more complex than simple self-attention. We built simulation and physical datasets using a DSR, and validated our method on the datasets. We obtained an accuracy of 82.5% for the physical dataset.</p>

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