Co-Scale Cross-Attentional Transformer for Rearrangement Target Detection

Autor:	Matsuo, Haruka, Ishikawa, Shintaro, Sugiura, Komei
Rok vydání:	2024
Předmět:	Computer Science - Robotics
Druh dokumentu:	Working Paper
Popis:	Rearranging objects (e.g. vase, door) back in their original positions is one of the most fundamental skills for domestic service robots (DSRs). In rearrangement tasks, it is crucial to detect the objects that need to be rearranged according to the goal and current states. In this study, we focus on Rearrangement Target Detection (RTD), where the model generates a change mask for objects that should be rearranged. Although many studies have been conducted in the field of Scene Change Detection (SCD), most SCD methods often fail to segment objects with complex shapes and fail to detect the change in the angle of objects that can be opened or closed. In this study, we propose a Co-Scale Cross-Attentional Transformer for RTD. We introduce the Serial Encoder which consists of a sequence of serial blocks and the Cross-Attentional Encoder which models the relationship between the goal and current states. We built a new dataset consisting of RGB images and change masks regarding the goal and current states. We validated our method on the dataset and the results demonstrated that our method outperformed baseline methods on $F_1$-score and mean IoU. Comment: Accepted for publication in Advanced Robotics
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.05063 Zobrazit plný text záznamu View this record from Arxiv