Official code and checkpoint release for mobile robot foundation models: GNM, ViNT, and NoMaD.

The Open Motion Planning Library (OMPL)

A Library for Advanced Deep Time Series Models.

ROSGPT is a pioneering approach that combines the power of ChatGPT and ROS (Robot Operating System) to redefine human-robot interaction. By leveraging large language models like ChatGPT, ROSGPT ena…

ROS 2 packages for CyberDog

OpenMMLab Detection Toolbox and Benchmark

[CoRL 2023] This repository contains data generation and training code for Scaling Up & Distilling Down

[RSS 2023] Diffusion Policy Visuomotor Policy Learning via Action Diffusion

A curated list of Diffusion Model in RL resources (continually updated)

清华主题PPT模板

Kolmogorov Arnold Networks

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

Common used path planning algorithms with animations.

Python package for the evaluation of odometry and SLAM

LiDAR SLAM = FAST-LIO + Scan Context

[CVPR 2024] Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. Foundation Model for Monocular Depth Estimation

(TPAMI 2024) A Survey on Open Vocabulary Learning

Collection of quadrupedal robots configured to work in CHAMP development framework

Real-time 3D localization using a (velodyne) 3D LIDAR

This is the official github repo of Think-on-Graph. If you are interested in our work or willing to join our research team in Shenzhen, please feel free to contact us by email ([email protected])

[CVPR 2023] Official implementation of the paper "One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer"

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

Open-source and strong foundation image recognition models.

PyTorch code for BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything