Papers with Code 아카이브

Robot Manipulation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

3

평가표

5

보존된 평가 기록

60

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Henry1iu/ierg5350_rl_course_project

Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks

https://github.com/HibaKob/MicroBundlePillarTrack

Segment Anything

https://github.com/IDEA-Research/Grounded-Segment-Anything

Segment Anything

https://github.com/JHUVisionLab/multi-modal-regression

A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images

https://github.com/Jianghanxiao/PhysTwin

PhysTwin: Physics-Informed Reconstruction and Simulation of Deformable Objects from Videos

https://github.com/LatentActionPretraining/LAPA

Latent Action Pretraining from Videos

https://github.com/MindCode-4/code-4/tree/main/sam

SAM 2: Segment Anything in Images and Videos

https://github.com/MindSpore-scientific/code-7/tree/main/SAM

SAM 2: Segment Anything in Images and Videos

https://github.com/NVlabs/oscar

OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation

https://github.com/PaddlePaddle/PaddleSeg

Segment Anything

https://github.com/RoboDita/Dita

Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action Policy

https://github.com/Robot-VLAs/RoboVLMs

Towards Generalist Robot Policies: What Matters in Building Vision-Language-Action Models

https://github.com/StanfordVL/arxivbot

Neural Task Programming: Learning to Generalize Across Hierarchical Tasks

https://github.com/TripleJoy/SAM2MOT

SAM 2: Segment Anything in Images and Videos

https://github.com/ZE-WEN/DendriteSAM

Segment Anything

https://github.com/abalakrishna123/recovery-rl

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

https://github.com/aim-uofa/matcher

Segment Anything

https://github.com/apple/ml-reed

Reward Uncertainty for Exploration in Preference-based Reinforcement Learning

https://github.com/arnab39/equiadapt

Segment Anything

https://github.com/beapc18/AffordanceNet

Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks

https://github.com/bowang-lab/medsam2

SAM 2: Segment Anything in Images and Videos

https://github.com/bytedance/gr-1

Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation

https://github.com/camenduru/grounded-segment-anything-colab

Segment Anything

https://github.com/chwilms/sos

Segment Anything

https://github.com/clvrai/skild

Demonstration-Guided Reinforcement Learning with Learned Skills

https://github.com/columbia-ai-robotics/dsr

Learning 3D Dynamic Scene Representations for Robot Manipulation

https://github.com/computational-cell-analytics/medico-sam

Segment Anything

https://github.com/computational-cell-analytics/micro-sam

Segment Anything

https://github.com/computational-cell-analytics/patho-sam

Segment Anything

https://github.com/computational-cell-analytics/peft-sam

Segment Anything

https://github.com/coradlut/bi-jros

Segment Anything

https://github.com/dair-iitd/nsrmp

Learning Neuro-symbolic Programs for Language Guided Robot Manipulation

https://github.com/dcnieho/segment-anything-2

SAM 2: Segment Anything in Images and Videos

https://github.com/eugeneteoh/greenaug

CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation Learning

https://github.com/geekyutao/inpaint-anything

Segment Anything

https://github.com/gidobot/SilhoNet

SilhoNet: An RGB Method for 6D Object Pose Estimation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.