---
license: other
tags:
- robotics
- reinforcement-learning
- locomotion
- quadruped
- wheel-legged-robot
- sim-to-real
- isaac-gym
- himloco
---

# FLORES HIMLoco Policy

This repository provides a pre-trained HIM-based locomotion policy checkpoint for **FLORES**, a reconfigured wheel-legged quadrupedal robot for enhanced steering and adaptability.

Paper: [A Reconfigured Wheel-Legged Robot for Enhanced Steering and Adaptability](https://arxiv.org/abs/2507.22345)  
GitHub: https://github.com/ZhichengSong6/FLORES

## Checkpoint

The provided checkpoint is:

```text
May22_0943.pt
````

This policy was trained for the FLORES `mdog` robot using the HIMLoco-based reinforcement learning pipeline.


## Observation and Action Space

The policy uses a 12-frame history of one-step observations. Each one-step observation has 57 dimensions:

```text
[0:3]    base angular velocity
[3:6]    projected gravity
[6:9]    velocity command: linear x, linear y, yaw
[9:25]   joint position error
[25:41]  joint velocity
[41:57]  previous action
```

The full policy observation has:

```text
57 × 12 = 684 dimensions
```

The latest one-step observation is placed at the front of the history buffer.

During training, the privileged observation has 250 dimensions:

```text
[0:57]     one-step policy observation
[57:60]    base linear velocity
[60:63]    external disturbance force
[63:250]   height measurements
```

The policy outputs 16 actions. The action order follows the FLORES / mdog joint order:

```text
[0]   FL_hip_joint
[1]   FL_thigh_joint
[2]   FL_calf_joint
[3]   FL_ankle_joint
[4]   FR_hip_joint
[5]   FR_thigh_joint
[6]   FR_calf_joint
[7]   FR_ankle_joint
[8]   RL_hip_joint
[9]   RL_thigh_joint
[10]  RL_calf_joint
[11]  RL_ankle_joint
[12]  RR_hip_joint
[13]  RR_thigh_joint
[14]  RR_calf_joint
[15]  RR_ankle_joint
```

The action scale is:

```text
action_scale = 0.25
```

Please verify the actual DOF order from the loaded URDF / Isaac Gym asset before deploying the policy on a modified robot model.

## Training Code

The FLORES environment files for HIMLoco are available in the GitHub repository:

```text
code/HIM_FLORES/
```

These files include:

```text
mdog_config.py
mdog_robot.py
```

To use them, first install [HIMLoco](https://github.com/InternRobotics/HIMLoco), then place the two files under:

```text
HIMLoco/legged_gym/legged_gym/envs/mdog/
```

Please refer to the GitHub README for detailed setup, task registration, and training instructions.

## Robot Asset

The policy is trained for the FLORES `mdog` model. The expected robot asset path in the training configuration is:

```text
HIMLoco/legged_gym/resources/robots/mdog/urdf/mdog.urdf
```

If the robot asset is placed elsewhere, please modify the asset path in `mdog_config.py`.

## Notes

* This `.pt` file is a pre-trained policy checkpoint used with the HIMLoco / legged_gym training setup.
* The current environment uses 16 actions corresponding to the robot joints.
* The checkpoint is intended to be used together with the FLORES GitHub repository and HIMLoco.
* If the robot model, observation structure, or terrain sensing setup is modified, the policy may need to be retrained or adjusted.

## Citation

If you find this work useful, please cite:

```bibtex
@ARTICLE{flores2026,
  author={Song, Zhicheng and Xu, Jinglan and Zheng, Chunxin and Li, Yulin and Bi, Zhihai and Ma, Jun},
  journal={IEEE Robotics and Automation Letters}, 
  title={A Reconfigured Wheel-Legged Robot for Enhanced Steering and Adaptability}, 
  year={2026},
  volume={11},
  number={6},
  pages={7444-7451},
  keywords={Legged robots;field robots},
  doi={10.1109/LRA.2026.3688384}}