Tiny Differentiable Simulator is a header-only C++ and CUDA physics library for reinforcement learning and robotics with zero dependencies.
Les fonctionnalités principales de erwincoumans/tiny-differentiable-simulator sont : Physics Simulation.
Les alternatives open-source à erwincoumans/tiny-differentiable-simulator incluent : genesis-embodied-ai/genesis-world — Genesis World is an embodied AI simulation platform designed for training robotic agents through physics-based… carla-simulator/carla — CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving… dusty-nv/jetson-inference — jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU… ayonga/frost-dev — Fast Robot Optimization and Simulation Toolkit (FROST). danielchappuis/reactphysics3d — Open source C++ physics engine library in 3D. bulletphysics/bullet3 — Bullet3 is a professional physics simulation engine designed for calculating rigid body, soft body, and collision…
CARLA is an autonomous driving simulator and research environment designed for developing and validating self-driving software. It functions as an urban traffic simulator that generates realistic vehicle and pedestrian behavior and as a synthetic sensor data generator producing LiDAR, Radar, and camera data. The platform distinguishes itself through its deep integration with robotics frameworks, specifically providing native connectivity to ROS2 nodes for robotic control and data processing. It supports the training of driving models via imitation and reinforcement learning within a controlle
Genesis World is an embodied AI simulation platform designed for training robotic agents through physics-based interactions. It centers on a multi-physics simulation engine that integrates rigid body, particle, and finite element method dynamics, supported by a parallel simulation kernel compiler that translates Python functions into optimized GPU and CPU kernels. The platform features a photorealistic robot renderer that utilizes path-tracing and Gaussian Splatting to generate synthetic training data. It includes a domain randomization framework to vary lighting and physical parameters acros
jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti
