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Action Generation

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

https://github.com/skelemoa/mugl

공식 코드

MUGL: Large Scale Multi Person Conditional Action Generation with Locomotion

https://github.com/ucla-mobility/AutoVLA

공식 코드

AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning

https://github.com/utomm/mini-diffuse-actor

공식 코드

Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batches

https://github.com/wayveai/driving-with-llms

공식 코드

Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving

https://github.com/wonderful-team-robotics/wonderful_team_robotics

공식 코드

Wonderful Team: Zero-Shot Physical Task Planning with Visual LLMs

https://github.com/xliu443/language-free-compositional-action-generation-via-decoupling-refinement

공식 코드

Language-free Compositional Action Generation via Decoupling Refinement

https://github.com/zheshiyige/Learning-Diverse-Stochastic-Human-Action-Generators-by-Learning-Smooth-Latent-Transitions

공식 코드

Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions

https://github.com/zhourobotics/llm-mrs-survey

공식 코드

Large Language Models for Multi-Robot Systems: A Survey

https://github.com/2024-MindSpore-1/Code9/tree/main/LOGO--Mindspore-master

FLAG3D: A 3D Fitness Activity Dataset with Language Instruction

https://github.com/MohammadrezaNakhaei/FQL

Flow Q-Learning

https://github.com/SHITIANYU-hue/Efficient-motion-planning

Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization

https://github.com/clic-lab/chalet

Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction

https://github.com/clic-lab/drif

Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction

https://github.com/lil-lab/chalet

Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction

https://github.com/lil-lab/ciff

Mapping Instructions to Actions in 3D Environments with Visual Goal Prediction

https://github.com/moojink/openvla-oft

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success

https://github.com/reallm-labs/infiguiagent

InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners

https://github.com/xingchenzhao/Generating-Human-Skeletons-with-Mutual-Actions-WGAN-Pytorch

Human Action Generation with Generative Adversarial Networks

https://github.com/xingchenzhao/deep-learning-team-project

Human Action Generation with Generative Adversarial Networks

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