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Robotic Grasping
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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This task is composed of using Deep Learning to identify how best to grasp objects using robotic arms in different scenarios. This is a very complex task as it might involve dynamic environments and objects unknown to the network.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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https://github.com/calmisential/TensorFlow2.0_InceptionV3
https://github.com/clvrai/furniture
IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
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https://github.com/danielenricocahall/Keras-UNet
https://github.com/edwardnguyen1705/robotic-grasping-cornell
https://github.com/fchollet/deep-learning-models
https://github.com/graspnet/graspnet-baseline
https://github.com/hamed-hosseini/ggcnn
Real-Time Grasp Detection Using Convolutional Neural Networks
https://github.com/harry24k/aepw-pytorch
https://github.com/hwzwx1190522/inceptionv3
https://github.com/hz-ants/arc-robot-vision
https://github.com/isaacschaal/SG_training
https://github.com/ivaROS/ros_deep_grasp
https://github.com/ivalab/grasp_multiObject_multiGrasp
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https://github.com/lbechberger/LearningPsychologicalSpaces
https://github.com/lennijusten/GradCam-InceptionV3
https://github.com/levanpon98/inception_v3
https://github.com/loevlie/DL_Project_PSMNet
https://github.com/lukoucky/image_recommendation
https://github.com/manashpratim/Flower_Recognition
https://github.com/marcojira/fld
https://github.com/marcojira/fls
https://github.com/marload/ConvNets-TensorFlow2
https://github.com/matthias-wright/jax-fid
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.