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legged robot environments for reinforcement learning in multiple simulators (IsaacGym, Genesis, IsaacSim)
| Date | Stars |
|---|---|
| 2026-07-31 | 343 |
| 2026-08-01 | 345 |
| 2026-08-02 | 345 |
| 2026-08-05 | 347 |
| 2026-08-06 | 347 |
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# 🦿 LeggedGym-Ex A [legged_gym](https://github.com/leggedrobotics/legged_gym) based framework for training legged robots in [Genesis](https://github.com/Genesis-Embodied-AI/Genesis/tree/main), [IsaacGym](https://developer.nvidia.com/isaac-gym) and [IsaacSim](https://developer.nvidia.com/isaac/sim). ## 🌟 Features - **Totally based on [legged_gym](https://github.com/leggedrobotics/legged_gym)** This framework keeps most apis and conventions consistent with legged_gym, providing good readability and better control over training pipeline. - **Integration of multiple simulators** We support training in either of three simulators: IsaacGym, Genesis and IsaacSim. Quick tips for choosing among three simulators: - Faster training but worse rendering -> IsaacGym - Both training speed and support for fluid, soft materials -> Genesis - More realistic rendering at the cost of training speed -> IsaacSim. - **Accelerated depth image rendering based on warp** We integrate [warp](https://github.com/nvidia/warp) into this framework to accelerate the depth image rendering on Nvidia GPU. - **Incorporation of various methods in published RL papers** | Method | Paper Link | Code | |--------|------------|----------| | Periodic Gait Reward | [Sim-to-Real Learning of All Common Bipedal Gaits via Periodic Reward Composition](https://arxiv.org/abs/2011.01387) | [go2_wtw](https://github.com/lupinjia/LeggedGym-Ex/blob/main/legged_gym/envs/go2/go2_wtw/go2_wtw.py#L322) | | Walk These Ways | [Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior](https://gmargo11.github.io/walk-these-ways/) | [go2_wtw](https://github.com/lupinjia/LeggedGym-Ex/blob/main/legged_gym/envs/go2/go2_wtw) | | System Identification | [Learning Agile Bipedal Motions on a Quadrupedal Robot](https://arxiv.org/abs/2311.05818) | [go2_sysid](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/go2/go2_sysid) | | Teacher-Student | [Rapid Locomotion via Reinforcement Learning](https://agility.csail.mit.edu/) | [go2_ts](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/go2/go2_ts) | | Explicit Estimator | [Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion](https://arxiv.org/abs/2202.05481) | [go2_ee](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/go2/go2_ee) | | Constraints as Terminations | [CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning](https://constraints-as-terminations.github.io/) | [go2_cat](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/go2/go2_cat) | | DreamWaQ | [DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning](https://arxiv.org/abs/2301.10602) | [go2_dreamwaq](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/go2/go2_dreamwaq) | | SPO (Simple Policy Optimization) | [Simple Policy Optimization](https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://github.com/MyRepositories-hub/Simple-Policy-Optimization&ved=2ahUKEwjL9vLX7auSAxVZlFYBHWkFBkIQFnoECBgQAQ&usg=AOvVaw1nGHIXtdYwpu3WV9lUgRWN) | [`legged_robot_config.py`](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/base/legged_robot_config.py) | | CTS (Concurrent Teacher Student) | [CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion](https://clearlab-sustech.github.io/concurrentTS/) | [go2_cts](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/go2/go2_cts) | | DeepMimic | [DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills](https://arxiv.org/abs/1804.02717) | [g1_deepmimic](https://github.com/lupinjia/LeggedGym-Ex/tree/main/legged_gym/envs/g1/g1_deepmimic) | | AMP (Adversarial Motion Priors) | [AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control](https://arxiv.org/ab
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matched fp:50d881f3fa885a0c, topic:reinforcement-learning, desc:reinforcement learning