# TITA_ISAACGYM_DRL

**Repository Path**: ddtrobot/TITA_ISAACGYM_DRL

## Basic Information

- **Project Name**: TITA_ISAACGYM_DRL
- **Description**: TITA Walking Reinforcement learning based on Issacgym 
- **Primary Language**: Unknown
- **License**: BSD-3-Clause
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-06-16
- **Last Updated**: 2026-06-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TITA(点足)isaac_gym强化学习#
**Contact**: 1692931357@qq.com
---

## Prerequisites

- **Operating System**: Ubuntu 22.04
- **isaac_gym**
- **legged_gym**

---

### Useful Links ###

使用教程: https://leggedrobotics.github.io/legged_gym/   


### Usage ###
1. Train:  
  ```python legged_gym/scripts/train.py --task=tita_pointfoot_rough```
    -  To run on CPU add following arguments: `--sim_device=cpu`, `--rl_device=cpu` (sim on CPU and rl on GPU is possible).
    -  To run headless (no rendering) add `--headless`.
    - **Important**: To improve performance, once the training starts press `v` to stop the rendering. You can then enable it later to check the progress.
    - The trained policy is saved in `logs/<experiment_name>/<date_time>_<run_name>/model_<iteration>.pt`. Where `<experiment_name>` and `<run_name>` are defined in the train config.
    -  The following command line arguments override the values set in the config files:
     - --task TASK: Task name.
     - --resume:   Resume training from a checkpoint
     - --experiment_name EXPERIMENT_NAME: Name of the experiment to run or load.
     - --run_name RUN_NAME:  Name of the run.
     - --load_run LOAD_RUN:   Name of the run to load when resume=True. If -1: will load the last run.
     - --checkpoint CHECKPOINT:  Saved model checkpoint number. If -1: will load the last checkpoint.
     - --num_envs NUM_ENVS:  Number of environments to create.
     - --seed SEED:  Random seed.
     - --max_iterations MAX_ITERATIONS:  Maximum number of training iterations.
2. Play a trained policy:  
```python legged_gym/scripts/play.py --task=pointfoot_rough --load_run <run_name> --checkpoint <checkpoint>```
    - By default, the loaded policy is the last model of the last run of the experiment folder.
    - Other runs/model iteration can be selected by setting `load_run` and `checkpoint` in the train config.
3. Export policy as an ONNX file:  
```python legged_gym/scripts/export_policy_as_onnx.py --task=pointfoot_flat```
    - By default, the loaded policy is the last model of the last run of the experiment folder.
    - Other runs/model iteration can be selected by setting `load_run` and `checkpoint` in the train config.
    - The exported onnx file is saved in `logs/<experiment_name>/export/policy.pt`.

### Adding a new environment ###
The base environment `legged_robot` implements a rough terrain locomotion task. The corresponding cfg does not specify a robot asset (URDF/ MJCF) and has no reward scales. 

1. Add a new folder to `envs/` with `'<your_env>_config.py`, which inherit from an existing environment cfgs  
2. If adding a new robot:
    - Add the corresponding assets to `resources/`.
    - In `cfg` set the asset path, define body names, default_joint_positions and PD gains. Specify the desired `train_cfg` and the name of the environment (python class).
    - In `train_cfg` set `experiment_name` and `run_name`
3. (If needed) implement your environment in <your_env>.py, inherit from an existing environment, overwrite the desired functions and/or add your reward functions.
4. Register your env in `legged_gym/envs/__init__.py`.
5. Modify/Tune other parameters in your `cfg`, `cfg_train` as needed. To remove a reward set its scale to zero. Do not modify parameters of other envs!


### Troubleshooting ###
1. If you get the following error: `ImportError: libpython3.8m.so.1.0: cannot open shared object file: No such file or directory`, do: `sudo apt install libpython3.8`. It is also possible that you need to do `export LD_LIBRARY_PATH=/path/to/libpython/directory` / `export LD_LIBRARY_PATH=/path/to/conda/envs/your_env/lib`(for conda user. Replace /path/to/ to the corresponding path.).

### Known Issues ###
1. The contact forces reported by `net_contact_force_tensor` are unreliable when simulating on GPU with a triangle mesh terrain. A workaround is to use force sensors, but the force are propagated through the sensors of consecutive bodies resulting in an undesirable behaviour. However, for a legged robot it is possible to add sensors to the feet/end effector only and get the expected results. When using the force sensors make sure to exclude gravity from the reported forces with `sensor_options.enable_forward_dynamics_forces`. Example:
```
    sensor_pose = gymapi.Transform()
    for name in feet_names:
        sensor_options = gymapi.ForceSensorProperties()
        sensor_options.enable_forward_dynamics_forces = False # for example gravity
        sensor_options.enable_constraint_solver_forces = True # for example contacts
        sensor_options.use_world_frame = True # report forces in world frame (easier to get vertical components)
        index = self.gym.find_asset_rigid_body_index(robot_asset, name)
        self.gym.create_asset_force_sensor(robot_asset, index, sensor_pose, sensor_options)
    (...)

    sensor_tensor = self.gym.acquire_force_sensor_tensor(self.sim)
    self.gym.refresh_force_sensor_tensor(self.sim)
    force_sensor_readings = gymtorch.wrap_tensor(sensor_tensor)
    self.sensor_forces = force_sensor_readings.view(self.num_envs, 4, 6)[..., :3]
    (...)

    self.gym.refresh_force_sensor_tensor(self.sim)
    contact = self.sensor_forces[:, :, 2] > 1.
```
# RobotRos2001