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Vision-Language-Action
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DriveVLA/OpenDriveVLA
공식 코드OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model
https://github.com/LukeLIN-web/VOTE
공식 코드VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
https://github.com/ManifoldRG/MultiNet
공식 코드Benchmarking Vision, Language, & Action Models on Robotic Learning Tasks
https://github.com/OpenHelix-robot/OpenHelix
공식 코드https://github.com/Physical-Intelligence/real-time-chunking-kinetix
공식 코드https://github.com/RoboDita/Dita
공식 코드https://github.com/Zhangwenyao1/DreamVLA
공식 코드DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
https://github.com/ahydchh/impromptu-vla
공식 코드Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models
https://github.com/alibaba-damo-academy/worldvla
공식 코드https://github.com/aoqunjin/awesome-vla-post-training
공식 코드Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trends
https://github.com/cccedric/conrft
공식 코드ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
https://github.com/cseeyangchen/c2vl
공식 코드https://github.com/eliotjones1/robogcg
공식 코드Adversarial Attacks on Robotic Vision Language Action Models
https://github.com/embodied-generalist/embodied-generalist
공식 코드https://github.com/gabinsane/multi-vaes-in-robotics
공식 코드Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks
https://github.com/ghy0501/awesome-continual-learning-in-generative-models
공식 코드A Comprehensive Survey on Continual Learning in Generative Models
https://github.com/guanxinglu/vlarl
공식 코드VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
https://github.com/huggingface/lerobot
공식 코드SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
https://github.com/huixin-zhan-ai/surgeon_style_fingerprinting
공식 코드https://github.com/jiaming-zhou/X-ICM
공식 코드Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization
https://github.com/johnsonjiang1996/awesome-vla4ad
공식 코드A Survey on Vision-Language-Action Models for Autonomous Driving
https://github.com/lcysyzxdxc/embodiediqa
공식 코드https://github.com/lostxine/llara
공식 코드LLaRA: Supercharging Robot Learning Data for Vision-Language Policy
https://github.com/mint-sjtu/robofac
공식 코드RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction
https://github.com/muhayyuddin/vlas
공식 코드Vision Language Action Models in Robotic Manipulation: A Systematic Review
https://github.com/opendrivelab/univla
공식 코드UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
https://github.com/pickxiguapi/embodied-fsd
공식 코드From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation
https://github.com/sautenich/uav-vla
공식 코드UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation
https://github.com/showlab/showui
공식 코드ShowUI: One Vision-Language-Action Model for GUI Visual Agent
https://github.com/tutujingyugang1/ChatVLA_public
공식 코드ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model
https://github.com/ucla-mobility/AutoVLA
공식 코드https://github.com/ustcwhy/bitvla
공식 코드BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation
https://github.com/waynemao/robomatrix
공식 코드https://github.com/william-wang618/roboticattack
공식 코드Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics
https://github.com/yueen-ma/awesome-vla
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.