Common used path planning algorithms with animations.
- astar
- anytime-repairing-astar
- learning-realtime-astar
- realtime-adaptive-astar
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Common used path planning algorithms with animations.
Unified framework for robot learning with multi-physics/renderer support
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).
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.
PyBullet Gymnasium environments for single and multi-agent reinforcement learning of quadcopter control
gradslam is an open source differentiable dense SLAM library for PyTorch
A generalized framework for prototyping full-stack cooperative driving automation applications under CARLA+SUMO.
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL
Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving
This repository contains multiple approaches for generating global racetrajectories.
Unmanned Surface Vehicle simulation on Gazebo with water current and winds
Python parser for URDFs
The first exam for code agents in the physical world: frozen robot tasks from many benchmark suites, each answered with a program and verified in a separate sandbox. Runs on Harbor.
RAFCON (RMC advanced flow control) uses hierarchical state machines, featuring concurrent state execution, to represent robot programs. It ships with a graphical user interface supporting the creation of state machines and contains IDE like debugging mechanisms. Alternatively, state machines can programmatically be generated using RAFCON's API.
Whole-body motion tracking for the Unitree G1 in under 3 minutes on average on one GPU: return 0.82 with 0/40 clips failed, held from 2.74 min (mean of 15 seeds, range 2.21-3.61, fine checkpoints), 11.87 it/s (world model + Muon + overlapped CUDA streams, mjlab)
Transformer-SRU LiDAR navigation for Unitree G1: Isaac Lab training, MuJoCo validation, and ROS 2 deployment.
CARLA-based cooperative perception system integrating YOLO26n, V2I communication, sensor fusion, TTC risk assessment, and automated braking.
A collection of machine-learned models for use in autonomous driving applications.
Python package for inverse kinematic calculations of hybrid serial parallel robots