Papers with Code 아카이브
Robotic Grasping
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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.
https://github.com/AnasIbrahim/image_agnostic_segmentation
공식 코드DoUnseen: Tuning-Free Class-Adaptive Object Detection of Unseen Objects for Robotic Grasping
https://github.com/DepthAnything/PromptDA
공식 코드Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
https://github.com/GeorgeDu/vision-based-robotic-grasping
공식 코드https://github.com/HuCaoFighting/DVS-GraspingDataSet
공식 코드Event-based Robotic Grasping Detection with Neuromorphic Vision Sensor and Event-Stream Dataset
https://github.com/IRVLUTD/SceneReplica
공식 코드SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes
https://github.com/JHKim-snu/PGA
공식 코드PGA: Personalizing Grasping Agents with Single Human-Robot Interaction
https://github.com/Johann-Huber/qd_grasp
공식 코드Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
https://github.com/NVlabs/contact_graspnet
공식 코드Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
https://github.com/PetropoulakisPanagiotis/igae
공식 코드https://github.com/StructuresComp/measure-anything
공식 코드Measure Anything: Real-time, Multi-stage Vision-based Dimensional Measurement using Segment Anything
https://github.com/THU-VCLab/HGGD
공식 코드Efficient Heatmap-Guided 6-Dof Grasp Detection in Cluttered Scenes
https://github.com/Taccel-Simulator/Taccel
공식 코드Taccel: Scaling Up Vision-based Tactile Robotics via High-performance GPU Simulation
https://github.com/ahundt/costar_dataset
공식 코드The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
https://github.com/ahundt/enas
공식 코드The CoSTAR Block Stacking Dataset: Learning with Workspace Constraints
https://github.com/anasibrahim/ctl_classification
공식 코드Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping
https://github.com/andrearosasco/hyperpcr
공식 코드Towards Confidence-guided Shape Completion for Robotic Applications
https://github.com/andrejorsula/drl_grasping
공식 코드Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning
https://github.com/andvg3/Grasp-Anything
공식 코드Grasp-Anything: Large-scale Grasp Dataset from Foundation Models
https://github.com/andyzeng/arc-robot-vision
공식 코드https://github.com/aspek1/QuadricCurvature
공식 코드A Fast Method For Computing Principal Curvatures From Range Images
https://github.com/augcog/dttdv1
공식 코드https://github.com/axkoenig/grasp_refinement
공식 코드The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms
https://github.com/baha2r/Fanuc_Robotiq_Grasp
공식 코드https://github.com/cliport/cliport
공식 코드https://github.com/cnjianliu/diff9d
공식 코드Diff9D: Diffusion-Based Domain-Generalized Category-Level 9-DoF Object Pose Estimation
https://github.com/cuge1995/u6da
공식 코드Adversarial samples for deep monocular 6D object pose estimation
https://github.com/ericcjoyce/consensus-driven-uncertainty
공식 코드Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
https://github.com/facebookresearch/ContactPose
공식 코드ContactPose: A Dataset of Grasps with Object Contact and Hand Pose
https://github.com/facebookresearch/pyrobot
공식 코드PyRobot: An Open-source Robotics Framework for Research and Benchmarking
https://github.com/galaxies99/transcg
공식 코드https://github.com/graspnet/anygrasp_sdk
공식 코드Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
https://github.com/hoenigpeter/randomized_texturing
공식 코드https://github.com/hp-cao/6impose
공식 코드6IMPOSE: Bridging the Reality Gap in 6D Pose Estimation for Robotic Grasping
https://github.com/imanlab/action_conditioned_tactile_prediction
공식 코드Action Conditioned Tactile Prediction: case study on slip prediction
https://github.com/isee-laboratory/economicgrasp
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.