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Robotic Grasping
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- 하위 태스크
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- 평가표
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- 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/jangerritha/CycleIK
공식 코드Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents
https://github.com/jbadiat/supertoroidfitting
공식 코드Supertoroid fitting of objects with holes for robotic grasping and scene generation
https://github.com/jhu-lcsr/costar_plan
공식 코드The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
https://github.com/kmittle/grasp-detection-nbmod
공식 코드https://github.com/korrawe/grasping_field
공식 코드Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/korrawe/grasping_field_demo
공식 코드Grasping Field: Learning Implicit Representations for Human Grasps
https://github.com/ld-ing/qdhf
공식 코드Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization
https://github.com/liruiw/GA-DDPG
공식 코드Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds
https://github.com/m4nh/loop
공식 코드Effective Deployment of CNNs for 3DoF Pose Estimation and Grasping in Industrial Settings
https://github.com/mahaoxiang822/generalizing-grasp
공식 코드Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge
https://github.com/mahaoxiang822/scale-balanced-grasp
공식 코드Towards Scale Balanced 6-DoF Grasp Detection in Cluttered Scenes
https://github.com/mees/calvin
공식 코드https://github.com/mrsecant/gaussiangrasper
공식 코드GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
https://github.com/nickgkan/orange
공식 코드Orientation Attentive Robotic Grasp Synthesis with Augmented Grasp Map Representation
https://github.com/ondrejba/action_priors
공식 코드https://github.com/otaheri/GRAB
공식 코드https://github.com/paul-shuvo/planar_pose
공식 코드https://github.com/poisonwine/regrad
공식 코드https://github.com/pouyaAB/Accept_Synthetic_Objects_as_Real
공식 코드https://github.com/priyankamandikal/graff
공식 코드Learning Dexterous Grasping with Object-Centric Visual Affordances
https://github.com/robertmccarthy97/rrc_phase1
공식 코드Solving the Real Robot Challenge using Deep Reinforcement Learning
https://github.com/samarth-robo/ContactPose-ML
공식 코드ContactPose: A Dataset of Grasps with Object Contact and Hand Pose
https://github.com/samarth-robo/contactdb_prediction
공식 코드ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging
https://github.com/samarth-robo/contactdb_utils
공식 코드ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging
https://github.com/sanket0707/bimodal-segnet
공식 코드Bimodal SegNet: Instance Segmentation Fusing Events and RGB Frames for Robotic Grasping
https://github.com/sanket0707/gnn-mixer
공식 코드Asynchronous Events-based Panoptic Segmentation using Graph Mixer Neural Network
https://github.com/saraao/amodal-dataset
공식 코드Amodal Intra-class Instance Segmentation: Synthetic Datasets and Benchmark
https://github.com/simpler-env/SimplerEnv
공식 코드Evaluating Real-World Robot Manipulation Policies in Simulation
https://github.com/skumra/baxter-pnp
공식 코드Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
https://github.com/skumra/robotic-grasping
공식 코드Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
https://github.com/stefan-ainetter/grasp_det_seg_cnn
공식 코드https://github.com/sunh18/monosim
공식 코드https://github.com/swtyree/hope-dataset
공식 코드https://github.com/tom1042roboai/aics_2022_causalcf
공식 코드Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
https://github.com/tom1042roboai/causalcf
공식 코드Causal Counterfactuals for Improving the Robustness of Reinforcement Learning
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