Open-source vacuum robot cleaner
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Open-source vacuum robot cleaner
Common used path planning algorithms with animations.
Unified framework for robot learning with multi-physics/renderer support
A Python framework for GPU-accelerated simulation, robotics, and machine learning.
[CVPR 2023 Best Paper Award] Planning-oriented Autonomous Driving
Dimensional is the agentic operating system for physical space. Command humanoids, quadrupeds, drones, and other hardware platforms in natural language and build multi-agent systems that work seamlessly with physical input (cameras, lidar, actuators).
Python package for the evaluation of odometry and SLAM
Manipulation Skill Framework, an open source GPU parallelized robotics simulator and benchmark
pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.
OpenCV Python Neural Network Autonomous RC Car
[CVPR 2025] MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors
Code to pretrain, fine-tune, and evaluate DreamZero and run sim & real-world evals
Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
Code and model to control the AH!
Spot SDK repo
ProtoMotions is a GPU-accelerated simulation and learning framework for training physically simulated digital humans and humanoid robots.
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
[CVPR'24 Highlight & Best Demo Award] Gaussian Splatting SLAM
NVIDIA Alpamayo 1 Nano is an open 10B reasoning VLA model for autonomous vehicles that pairs driving trajectories with Chain-of-Causation reasoning.
A collection of robotics simulation environments for reinforcement learning
Learn autonomous driving by building it: Python simulations and an open textbook where the system grows one stage at a time. Swap and compare localization, planning and control algorithms in the same system.
Collections of robotics environments geared towards benchmarking multi-task and meta reinforcement learning
A large-scale benchmark and learning environment.