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Deep RL for MPC control of Quadruped Robot Locomotion
| Date | Stars |
|---|---|
| 2026-07-24 | 1008 |
| 2026-07-25 | 1008 |
| 2026-07-28 | 1010 |
| 2026-07-30 | 1010 |
| 2026-08-06 | 1010 |
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0.0
growth rate 0.00%/day
# RL MPC Locomotion
This repo aims to provide a fast simulation and RL training framework for a quadruped locomotion task by dynamically predicting the weight parameters of a MPC controller. The control framework is a hierarchical controller composed of a higher-level policy network and a lower-level model predictive controller.
The MPC controller refers to [Cheetah Software](https://github.com/mit-biomimetics/Cheetah-Software) but written in python, and it completely opens the interface between sensor data and motor commands, so that the controller can be easily ported to any mainstream simulators.
The RL training utilizes the [NVIDIA Isaac Gym](https://developer.nvidia.com/isaac-gym) in parallel using Unitree Robotics Aliengo model, and transferring it from simulation to reality on a [real Aliengo robot](#sim2real_anchor) (sim2real is not included in this codebase).
## Frameworks
<img src="images/controller_blocks.png" width=700>
## Dependencies
- *Python* - 3.8
- [*PyTorch* - 1.10.0 with CUDA 11.3](https://pytorch.org/get-started/previous-versions/)
- [*Isaac Gym* - Preview 4](https://developer.nvidia.com/isaac-gym)
<!-- - *OSQP* - 0.6.2 -->
## Installation
1. Clone this repository
```bash
git clone [email protected]:silvery107/rl-mpc-locomotion.git
```
2. Initialize submodules
```bash
git submodule update --init
```
Or use the `--recurse` option in step 1 to clone submodules at the same time.
3. Create the conda environment:
```bash
conda env create -f environment.yml
```
4. Install `rsl_rl` at commit [2ad79cf](2ad79cf0caa85b91721abfe358105f869a784121) under `<extern>` folder
```bash
cd extern/rsl_rl
pip install -e .
```
5. Compile python binding of the MPC solver:
```bash
pip install -e .
```
## Quick Start
1. Play the MPC controller on Aliengo:
```bash
python RL_MPC_Locomotion.py --robot=Aliengo
```
All supported robot types are `Go1`, `A1` and `Aliengo`.
Note that you need to plug in your Xbox-like gamepad to control it, or pass `--disable-gamepad`.
The controller mode is default to `Fsm` (Finite State Machine), and you can also try `Min` for the minimum MPC controller without FSM.
- Gamepad keymap
> Press `LB` to switch gait types between `Trot`, `Walk` and `Bound`.
> Press `RB` to switch FSM states between `Locomotion` and `Recovery Stand`
2. Train a new policy:
```bash
cd RL_Environment
python train.py task=Aliengo headless=False
```
Press the `v` key to disable viewer updates, and press again to resume.
Set `headless=True` to train without rendering.
Tensorboard support is available, run `tensorboard --logdir runs`.
3. Load a pretrained checkpoint:
```bash
python train.py task=Aliengo checkpoint=runs/Aliengo/nn/Aliengo.pth test=True num_envs=4
```
Set `test=False` to continue training.
4. Run the pretrained weight-policy for MPC controller on Aliengo:
Set `bridge_MPC_to_RL` to `False` in `<MPC_Controller/Parameters.py>`
```bash
python RL_MPC_Locomotion.py --robot=Aliengo --mode=Policy --checkpoint=path/to/ckpt
```
If no `checkpoint` is given, it will load the latest run.
## Roadmap
<img src="images/MPC_block.png" width=600>
- [x] **MPC Controller**
- [Quadruped](MPC_Controller/common/Quadruped.py),
- [RobotRunner](MPC_Controller/robot_runner/RobotRunnerFSM.py) ->
- [LegController](MPC_Controller/common/LegController.py),
- [StateEstimator](MPC_Controller/common/StateEstimator.py),
- [ControlFSM](MPC_Controller/FSM_states/ControlFSM.py) ->
- [FSM State RecoveryStand](MPC_Controller/FSM_states/FSM_State_RecoveryStand.py),
- [FSM State Locomotion](MPC_Controller/FSM_states/FSM_State_Locomotion.py) ->
- [ConvexMPCLocomotion](MPC_Controller/convex_MPC/ConvexMPCLocomotion.py) ->
- [FootSwingTrajectory](MPC_Controller/common/FootSwingTrajectory.py),
- [Gait](MPC_ContrExcerpt of 4,994 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:6383cd21c6fa1cc0, topic:reinforcement-learning, readme:rl environment
matched fp:6383cd21c6fa1cc0, topic:robotics, readme:robotics