【具身智能/自主导航】相关开源项目代码、论文收集

【代码】具身智能、自主导航相关代码、论文集合。

我才是一卓

1819人浏览 · 2023-10-09 14:35:33

我才是一卓 · 2023-10-09 14:35:33 发布

1. 代码集合

Awesome-LLM-Robotics: https://github.com/GT-RIPL/Awesome-LLM-Robotics
Everything-LLMs-And-Robotics: https://github.com/jrin771/Everything-LLMs-And-Robotics
Awesome LLM-Powered Agent: https://github.com/hyp1231/awesome-llm-powered-agent
awesome-embodied-vision: https://github.com/ChanganVR/awesome-embodied-vision

2. 论文集合

CoRL | OpenReview: https://openreview.net/group?id=robot-learning.org/CoRL
Embodied AI Workshop (CVPR): https://embodied-ai.org/
Springer: Autonomous Robots: https://link.springer.com/journal/10514/collections
Habitat: A Platform for Embodied AI Research: https://aihabitat.org/

3. 自主导航强相关项目

LM-Nav: Robotic Navigation with Large Pre-Trained Models: https://sites.google.com/view/lmnav/home
Visual Language Maps for Robot Navigation: https://vlmaps.github.io/
ConceptFusion: Open-set Multimodal 3D Mapping: https://concept-fusion.github.io/
ESC: Exploration with Soft Commonsense Constraints for Zero-shot Object Navigation: https://sites.google.com/ucsc.edu/escnav/home
ViNG: Learning Open-World Navigation with Visual Goals: https://sites.google.com/view/ving-robot/
RECON: Learning to Explore the Real World with a Ground Robot：https://sites.google.com/view/recon-robot
ViKiNG: Vision-Based Kilometer-Scale Navigation with Geographic Hints: https://sites.google.com/view/viking-release
General Navigation Models: https://general-navigation-models.github.io/

4. 其他相关项目

CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory: https://mahis.life/clip-fields/
CoWs on Pasture: Baselines and Benchmarks for Language-Driven Zero-Shot Object Navigation: https://cow.cs.columbia.edu/
LERF: Language Embedded Radiance Fields: https://www.lerf.io/- Audio Visual Language Maps for Robot Navigation": https://avlmaps.github.io/
Simple but Effective: CLIP Embeddings for Embodied AI: https://github.com/allenai/embodied-clip
Semantic Abstraction: Open-World 3D Scene Understanding from 2D Vision-Language Models: https://semantic-abstraction.cs.columbia.edu/

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