🦿 LeggedGym-Ex

May 20, 2026 · View on GitHub

A legged_gym based framework for training legged robots in Genesis, IsaacGym and IsaacSim.

🌟 Features

Totally based on 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 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	go2_wtw
Walk These Ways	Walk These Ways: Tuning Robot Control for Generalization with Multiplicity of Behavior	go2_wtw
System Identification	Learning Agile Bipedal Motions on a Quadrupedal Robot	go2_sysid
Teacher-Student	Rapid Locomotion via Reinforcement Learning	go2_ts
Explicit Estimator	Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion	go2_ee
Constraints as Terminations	CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning	go2_cat
DreamWaQ	DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning	go2_dreamwaq
SPO (Simple Policy Optimization)	Simple Policy Optimization	`legged_robot_config.py`
CTS (Concurrent Teacher Student)	CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion	go2_cts
DeepMimic	DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills	g1_deepmimic
AMP (Adversarial Motion Priors)	AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control	k1_amp

Please refer to the doc of this repo.

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