The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
성공적인 로봇 조작을 위해서는 파지 지점을 정확하고 빠르게 예측하는 것이 중요합니다. 그러나 상업용 주방에 식기세척기 로봇과 같은 로봇을 상업적으로 배포하려면 제한된 사용 가능한 리소스의 제약도 고려해야 합니다. 물체를 집기 위해 단일 흡입 그리퍼를 사용할 때 파지 위치를 예측하는 딥러닝 방법을 제시합니다. 제안된 방법은 얕은 네트워크를 기반으로 하여 훈련 비용을 낮추고 제한된 자원에서 효율적인 추론을 가능하게 합니다. 맞춤형 합성 환경에서 데이터를 수집하면 비용이 더욱 절감됩니다. 제안된 방법을 평가하기 위해 우리는 식기세척기 로봇이 대칭 객체를 조작하는 상업용 주방을 모델링하는 시스템을 개발했습니다. 우리는 개발된 상업용 주방 환경에서 모델 피팅 방법과 알고리즘 기반 방법에 대해 방법을 테스트한 결과 합성 데이터로만 훈련된 얕은 네트워크가 높은 정확도를 달성한다는 것을 발견했습니다. 또한 학습 용이성, 예측 속도, 낮은 계산 비용 및 보다 쉬운 디버깅을 위해 객체 감지기와 함께 얕은 네트워크를 순차적으로 사용하는 실용성을 보여줍니다.
Suraj Prakash PATTAR
Connected Robotics Inc.
Tsubasa HIRAKAWA
Chubu University
Takayoshi YAMASHITA
Chubu University
Tetsuya SAWANOBORI
Connected Robotics Inc.
Hironobu FUJIYOSHI
Chubu University
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Suraj Prakash PATTAR, Tsubasa HIRAKAWA, Takayoshi YAMASHITA, Tetsuya SAWANOBORI, Hironobu FUJIYOSHI, "Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 9, pp. 1600-1609, September 2022, doi: 10.1587/transinf.2022EDK0001.
Abstract: Predicting the grasping point accurately and quickly is crucial for successful robotic manipulation. However, to commercially deploy a robot, such as a dishwasher robot in a commercial kitchen, we also need to consider the constraints of limited usable resources. We present a deep learning method to predict the grasp position when using a single suction gripper for picking up objects. The proposed method is based on a shallow network to enable lower training costs and efficient inference on limited resources. Costs are further reduced by collecting data in a custom-built synthetic environment. For evaluating the proposed method, we developed a system that models a commercial kitchen for a dishwasher robot to manipulate symmetric objects. We tested our method against a model-fitting method and an algorithm-based method in our developed commercial kitchen environment and found that a shallow network trained with only the synthetic data achieves high accuracy. We also demonstrate the practicality of using a shallow network in sequence with an object detector for ease of training, prediction speed, low computation cost, and easier debugging.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDK0001/_p
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@ARTICLE{e105-d_9_1600,
author={Suraj Prakash PATTAR, Tsubasa HIRAKAWA, Takayoshi YAMASHITA, Tetsuya SAWANOBORI, Hironobu FUJIYOSHI, },
journal={IEICE TRANSACTIONS on Information},
title={Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data},
year={2022},
volume={E105-D},
number={9},
pages={1600-1609},
abstract={Predicting the grasping point accurately and quickly is crucial for successful robotic manipulation. However, to commercially deploy a robot, such as a dishwasher robot in a commercial kitchen, we also need to consider the constraints of limited usable resources. We present a deep learning method to predict the grasp position when using a single suction gripper for picking up objects. The proposed method is based on a shallow network to enable lower training costs and efficient inference on limited resources. Costs are further reduced by collecting data in a custom-built synthetic environment. For evaluating the proposed method, we developed a system that models a commercial kitchen for a dishwasher robot to manipulate symmetric objects. We tested our method against a model-fitting method and an algorithm-based method in our developed commercial kitchen environment and found that a shallow network trained with only the synthetic data achieves high accuracy. We also demonstrate the practicality of using a shallow network in sequence with an object detector for ease of training, prediction speed, low computation cost, and easier debugging.},
keywords={},
doi={10.1587/transinf.2022EDK0001},
ISSN={1745-1361},
month={September},}
부
TY - JOUR
TI - Single Suction Grasp Detection for Symmetric Objects Using Shallow Networks Trained with Synthetic Data
T2 - IEICE TRANSACTIONS on Information
SP - 1600
EP - 1609
AU - Suraj Prakash PATTAR
AU - Tsubasa HIRAKAWA
AU - Takayoshi YAMASHITA
AU - Tetsuya SAWANOBORI
AU - Hironobu FUJIYOSHI
PY - 2022
DO - 10.1587/transinf.2022EDK0001
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2022
AB - Predicting the grasping point accurately and quickly is crucial for successful robotic manipulation. However, to commercially deploy a robot, such as a dishwasher robot in a commercial kitchen, we also need to consider the constraints of limited usable resources. We present a deep learning method to predict the grasp position when using a single suction gripper for picking up objects. The proposed method is based on a shallow network to enable lower training costs and efficient inference on limited resources. Costs are further reduced by collecting data in a custom-built synthetic environment. For evaluating the proposed method, we developed a system that models a commercial kitchen for a dishwasher robot to manipulate symmetric objects. We tested our method against a model-fitting method and an algorithm-based method in our developed commercial kitchen environment and found that a shallow network trained with only the synthetic data achieves high accuracy. We also demonstrate the practicality of using a shallow network in sequence with an object detector for ease of training, prediction speed, low computation cost, and easier debugging.
ER -