[2J1-GS-8a] ロボットと実世界：人との協同

[2J1-GS-8a-03] Collision Risk Prediction and Visualization Based on Transformer PonNet in Object Placement Tasks by Domestic Service Robots

〇Arisa Ueda¹, Magassouba Aly³, Tubasa Hirakawa², Takayoshi Yamashita², Hironobu Hujiyosi², Komei Sugiura¹ (1. Keio University, 2. Chubu University, 3. National Institute of Information and Communications Technology)

Keywords:Object manipulation, Attention Branch Network, Sim2Real

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.