<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Simulation on Noureddine RAMDI</title><link>https://ramdi.fr/tags/simulation/</link><description>Recent content in Simulation on Noureddine RAMDI</description><generator>Hugo</generator><language>en</language><lastBuildDate>Sat, 23 May 2026 20:41:27 +0000</lastBuildDate><atom:link href="https://ramdi.fr/tags/simulation/index.xml" rel="self" type="application/rss+xml"/><item><title>Exploring autonomy_stack_go2: A ROS2 and Unity-based autonomous vehicle simulation stack</title><link>https://ramdi.fr/github-stars/exploring-autonomy-stack-go2-a-ros2-and-unity-based-autonomous-vehicle-simulation-stack/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/exploring-autonomy-stack-go2-a-ros2-and-unity-based-autonomous-vehicle-simulation-stack/</guid><description>autonomy_stack_go2 is a C++ ROS2 stack integrated with Unity for autonomous vehicle simulation. Supports ROS2 Foxy and Humble with waypoint navigation in RVIZ.</description></item><item><title>Genesis-world: a high-throughput unified physics engine for robotics simulation and embodied AI</title><link>https://ramdi.fr/github-stars/genesis-world-a-high-throughput-unified-physics-engine-for-robotics-simulation-and-embodied-ai/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/genesis-world-a-high-throughput-unified-physics-engine-for-robotics-simulation-and-embodied-ai/</guid><description>Genesis-world delivers 43 million FPS on RTX 4090, unifying multiple physics methods with GPU acceleration and a pythonic API. It supports differentiable sim and natural language-driven data generation for robotics.</description></item><item><title>GS-Playground: High-throughput photorealistic simulation for vision-based robot learning</title><link>https://ramdi.fr/github-stars/gs-playground-high-throughput-photorealistic-simulation-for-vision-based-robot-learning/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/gs-playground-high-throughput-photorealistic-simulation-for-vision-based-robot-learning/</guid><description>GS-Playground combines 3D Gaussian Splatting rendering with a velocity-impulse physics engine to enable large-scale visual reinforcement learning at up to 10^4 FPS. Preview release with core simulation API and demos.</description></item><item><title>Inside NavDP: A diffusion policy approach to mapless robot navigation with sim-to-real transfer</title><link>https://ramdi.fr/github-stars/inside-navdp-a-diffusion-policy-approach-to-mapless-robot-navigation-with-sim-to-real-transfer/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/inside-navdp-a-diffusion-policy-approach-to-mapless-robot-navigation-with-sim-to-real-transfer/</guid><description>NavDP uses a diffusion policy architecture with privileged information to achieve mapless robot navigation across simulated and real environments without real-world training data.</description></item><item><title>OpenSim-core: a C++ musculoskeletal simulation engine with Python and Java bindings</title><link>https://ramdi.fr/github-stars/opensim-core-a-c-musculoskeletal-simulation-engine-with-python-and-java-bindings/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/opensim-core-a-c-musculoskeletal-simulation-engine-with-python-and-java-bindings/</guid><description>OpenSim-core is an open-source C++ library for musculoskeletal modeling and dynamic simulations, with Python and Java bindings for scripting complex biomechanics analyses.</description></item><item><title>scenario-lab: a Python CLI tool for scenario simulation workflows</title><link>https://ramdi.fr/github-stars/scenario-lab-a-python-cli-tool-for-scenario-simulation-workflows/</link><pubDate>Sat, 23 May 2026 20:41:14 +0000</pubDate><guid>https://ramdi.fr/github-stars/scenario-lab-a-python-cli-tool-for-scenario-simulation-workflows/</guid><description>scenario-lab is a Python-based tool for running scenario simulations via a CLI, emphasizing reproducible workflows and modular structure with Python 3.12 venv support.</description></item><item><title>Inside Genie Sim 3.0: LLM-driven embodied AI simulation with high-fidelity 3D scenes</title><link>https://ramdi.fr/github-stars/inside-genie-sim-3-0-llm-driven-embodied-ai-simulation-with-high-fidelity-3d-scenes/</link><pubDate>Tue, 05 May 2026 16:46:42 +0000</pubDate><guid>https://ramdi.fr/github-stars/inside-genie-sim-3-0-llm-driven-embodied-ai-simulation-with-high-fidelity-3d-scenes/</guid><description>Genie Sim 3.0 is an open-source platform combining 3D Gaussian Splatting and LLM-driven scene generation for embodied AI simulation, offering large-scale synthetic data and low sim-to-real discrepancy.</description></item><item><title>Recreating the 3dfx Voodoo GPU in SpinalHDL for FPGA and cycle-accurate simulation</title><link>https://ramdi.fr/github-stars/recreating-the-3dfx-voodoo-gpu-in-spinalhdl-for-fpga-and-cycle-accurate-simulation/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/recreating-the-3dfx-voodoo-gpu-in-spinalhdl-for-fpga-and-cycle-accurate-simulation/</guid><description>SpinalVoodoo rebuilds the classic 3dfx Voodoo Graphics GPU in SpinalHDL, targeting FPGA synthesis and cycle-accurate simulation with a focus on perspective-corrected texture mapping and fixed-point interpolation.</description></item><item><title>SimScale: a scalable sim-real co-training pipeline for autonomous driving planners</title><link>https://ramdi.fr/github-stars/simscale-a-scalable-sim-real-co-training-pipeline-for-autonomous-driving-planners/</link><pubDate>Mon, 04 May 2026 10:23:02 +0000</pubDate><guid>https://ramdi.fr/github-stars/simscale-a-scalable-sim-real-co-training-pipeline-for-autonomous-driving-planners/</guid><description>SimScale provides a sim-real co-training pipeline for autonomous driving planners, combining synthetic simulation data with real-world data to improve robustness and generalization across multiple planner types.</description></item><item><title>A hands-on guide to classical autonomous vehicle control algorithms in Python</title><link>https://ramdi.fr/github-stars/a-hands-on-guide-to-classical-autonomous-vehicle-control-algorithms-in-python/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/a-hands-on-guide-to-classical-autonomous-vehicle-control-algorithms-in-python/</guid><description>Explore a Python repo implementing classical autonomous vehicle algorithms as transparent simulations. Covers localization, mapping, planning, and path tracking with visualizations.</description></item><item><title>Inside Asimov v1: an open-source humanoid robot with dual-compute control and MuJoCo simulation</title><link>https://ramdi.fr/github-stars/inside-asimov-v1-an-open-source-humanoid-robot-with-dual-compute-control-and-mujoco-simulation/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/inside-asimov-v1-an-open-source-humanoid-robot-with-dual-compute-control-and-mujoco-simulation/</guid><description>Asimov v1 is an open-source 1.2m humanoid robot with dual-compute architecture and MuJoCo simulation. Explore its hardware design, CAN bus system, and simulation model for robotics development.</description></item><item><title>SimRecon: compositional 3D scene reconstruction with viewpoint optimization and semantic graph synthesis</title><link>https://ramdi.fr/github-stars/simrecon-compositional-3d-scene-reconstruction-with-viewpoint-optimization-and-semantic-graph-synthesis/</link><pubDate>Mon, 04 May 2026 10:23:01 +0000</pubDate><guid>https://ramdi.fr/github-stars/simrecon-compositional-3d-scene-reconstruction-with-viewpoint-optimization-and-semantic-graph-synthesis/</guid><description>SimRecon converts real-world videos into simulation-ready 3D scenes by combining geometry reconstruction, instance segmentation, viewpoint optimization, and semantic scene graph synthesis.</description></item></channel></rss>