Awesome
Awesome NVIDIA Isaac Gym 🤖
A curated collection of resources related to NVIDIA Isaac Gym, a high-performance GPU-based physics simulation environment for robot learning.
🎯 Quick Links
📋 Contents
🚀 Latest Releases
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February 2024: Isaac Lab - A unified and modular framework for robot learning (Website)
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February 2024: PhysX 5 SDK release (GitHub)
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February 2022: Isaac Gym Preview 4 (1.3.0)
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October 2021: Isaac Gym Preview 3
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June 2021: NVIDIA Isaac Sim on Omniverse Open Beta
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March 23, 2022: GTC 2022 Session — Isaac Gym: The Next Generation — High-performance Reinforcement Learning in Omniverse.
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Isaac Gym Overview: Isaac Gym Session.
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GTC Spring 2021: Isaac Gym: End-to-End GPU-Accelerated Reinforcement Learning.
🎓 Getting Started
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Installation & Setup
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Basic Concepts
📚 Official Resources
Core Documentation
Learning Resources
📖 Learning Materials
Tutorials
Comprehensive tutorial series from RSS 2021 Workshop:
- Introduction & Getting Started
- Environments, Training & Tips
- Academic Labs Series:
- New Frontiers in GPU Accelerated RL
Video Guides
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From Point Clouds to Material Graphs: Explore the Latest in Omniverse Create 2021.3
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Can We Simulate a Real Robot? — A journey through finding a high-quality physics simulator for a robot quadruped.
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Teaching Robots to Walk with Reinforcement Learning — Robot simulation adventure, covering reinforcement learning with the Bittle robot.
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Robot Dog Learns to Walk — Bittle Reinforcement Learning Part 3 — Further progress in training robot quadrupeds to walk.
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Isaac Sim GTC 2021 — Sim-to-Real: Session on sim-to-real transfer using Isaac Sim.
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Isaac Sim Video Tutorials: Official video tutorials.
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Training Your JetBot in NVIDIA Isaac Sim: Guide on training JetBot using Isaac Sim.
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Training Your NVIDIA JetBot to Avoid Collisions Using NVIDIA Isaac Sim: Blog post on collision avoidance training.
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Introducing NVIDIA Isaac Gym: End-to-End Reinforcement Learning for Robotics: Introduction to Isaac Gym.
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Accelerating Robotics Simulation with NVIDIA Omniverse Isaac Sim: Blog post on using Omniverse with Isaac Sim.
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Developing Robotics Applications in Python with NVIDIA Isaac SDK: Guide on using Isaac SDK with Python.
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Building an Intelligent Robot Dog with the NVIDIA Isaac SDK: Tutorial on building a robot dog.
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NVIDIA Omniverse YouTube Channel: Official channel with various tutorials and demos.
Blogs
📑 Research Papers
Core Papers
- Isaac Gym: High Performance GPU-Based Physics Simulation (NeurIPS 2021)
Robot Manipulation
- RLAfford: Official implementation of "RLAfford: End-to-end Affordance Learning with Reinforcement Learning", ICRA 2023.
- Masked Visual Pre-training for Robotics (MVP): Repository for the MVP project.
- Factory: Fast Contact for Robotic Assembly: RSS 2022.
- ASE: Adversarial Skill Embeddings: SIGGRAPH 2022.
- Data-Driven Operational Space Control (OSCAR): Adaptive and robust robot manipulation.
- DefGraspSim: Simulation-based grasping of deformable objects.
- In-Hand Object Pose Tracking: ICRA 2021.
- STORM: Fast Joint-Space MPC for Reactive Manipulation: CoRL 2021.
- Transferring Dexterous Manipulation from GPU Simulation to Real-World TriFinger:
- Causal Reasoning in Simulation for Robot Manipulation Policies: ICRA 2021.
- Reactive Long Horizon Task Execution: IROS 2021.
Localization & Control
- Learning to Walk in Minutes Using Massively Parallel Deep RL: CoRL 2021.
- Dynamics Randomization Revisited: A case study for quadrupedal locomotion.
- GLiDE: Generalizable Quadrupedal Locomotion:
- Learning a Contact-Adaptive Controller: For robust, efficient legged locomotion.
- Learning a State Representation and Navigation: In cluttered and dynamic environments.
Others
- BayesSimIG: Scalable parameter inference for adaptive domain randomization with Isaac Gym.
- Isaac Gym: High Performance GPU-Based Physics Simulation: NeurIPS 2021.
- Learning to Swim: Reinforcement learning for 6-DOF control of thruster-driven AUVs.
- MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation: Based on Issac Sim
- space_robotics_bench Space Robotics Bench
- Humanoid-Gym: Humanoid-Gym: Reinforcement Learning for Humanoid Robot with Zero-Shot Sim2Real Transfer
🛠 Tools & Libraries
RL Frameworks
- RL Games - Compatible RL algorithms
- ElegantRL
- skrl - Modular RL library
- Minimal Stable PPO
Community Projects
- IsaacGymEnvs: Official Isaac Gym RL environments.
- isaacgym_hammering: Hammering task implementation.
- isaacgym-utils: Utilities by CMU's Intelligent Autonomous Manipulation Lab.
- isaacgym_sandbox: Sandbox for Isaac Gym experiments.
- thormang3-gogoro-PPO: Two-wheeled vehicle control using PPO.
- Bez_IsaacGym: Environments for humanoid robot Bez.
- DexterousHands: Dual dexterous hand manipulation tasks.
- legged_gym_isaac: Legged robots in Isaac Gym.
- shifu: Environment builder for any robot.
- Rofunc: Python package for robot learning from demonstration.
- Dofbot Reacher: Sim2Real environment for Dofbot.
- UR10 Reacher: Sim2Real environment for UR10.
- TimeChamber: Massively parallel self-play framework.
- RL-MPC-Locomotion: Deep RL for quadruped locomotion.
- Isaac_Underwater: Water and underwater tests using NVIDIA Isaac Sim.
- VRKitchen2.0-IndoorKit: Omniverse IndoorKit Extension.
- agibot_x1_train: The reinforcement learning training code for AgiBot X1.
Conference Sessions and Talks
- Isaac Gym and Omniverse: High Performance Reinforcement Learning Evolved [A31118]
- Learning Challenging Tasks for Quadrupedal Robots: From Simulation to Reality [A31308]
- Sim-to-Real in Isaac Sim
- Isaac Gym: End-to-End GPU-Accelerated Reinforcement Learning
- Bridging Sim2Real Gap: Simulation Tuning for Training Deep Learning Robotic Perception Models
- Reinforcement Learning and Intralogistics
- Building Robotics Applications Using NVIDIA Isaac SDK
- NVIDIA Isaac Sim — Amazing Robot Models and Tasks
- Omniverse View 2021.2 — Application Tour
- ISAAC SIM Introduction and Live Demo
- NVIDIA On-Demand ISAAC SIM Sessions
🌟 Contributing
Contributions are welcome! Please read our contribution guidelines before submitting a pull request.
📄 License
This repository is licensed under the MIT License - see the LICENSE file for details.
🙏 Acknowledgments
Special thanks to all contributors and the NVIDIA Isaac team for making these resources available to the robotics community.