https://github.com/swchui/VTFSA
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
Papers with Code 아카이브
Robotic Grasping
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 4
- 보존된 평가 기록
- 19
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.
https://github.com/tensorflow/models/tree/master/research/im2txt
https://github.com/tensorflow/models/tree/master/research/marco
https://github.com/tensorflow/models/tree/master/research/slim
https://github.com/thang86/thang86.github.io
https://github.com/uclaml/Frank-Wolfe-AdvML
https://github.com/ukachyuthan/Rewards_in_RL
https://github.com/wenbowen123/BundleTrack
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
https://github.com/wq13552463699/rrc_pos_ori
Solving the Real Robot Challenge using Deep Reinforcement Learning
https://github.com/yanmin-wu/EAO-SLAM
https://github.com/youngsean/unseenobjectswithmeanshift
Self-Supervised Unseen Object Instance Segmentation via Long-Term Robot Interaction
https://github.com/zerchen/AlignSDF
Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/zerchen/gSDF
Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/zjZSTU/GoogLeNet
https://github.com/zy1998/inception_v3_flowerIdentify
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.