legged-gym包

资源文件列表(大概)

文件名

大小

legged_gym-master/

legged_gym-master/.gitattributes

148B

legged_gym-master/.github/

legged_gym-master/.github/ISSUE_TEMPLATE/

legged_gym-master/.github/ISSUE_TEMPLATE/bug_report.md

654B

legged_gym-master/.gitignore

690B

legged_gym-master/LICENSE

1.69KB

legged_gym-master/README.md

7.08KB

legged_gym-master/legged_gym/

legged_gym-master/legged_gym/__init__.py

1.79KB

legged_gym-master/legged_gym/envs/

legged_gym-master/legged_gym/envs/__init__.py

2.71KB

legged_gym-master/legged_gym/envs/a1/

legged_gym-master/legged_gym/envs/a1/a1_config.py

3.62KB

legged_gym-master/legged_gym/envs/anymal_b/

legged_gym-master/legged_gym/envs/anymal_b/anymal_b_config.py

2.2KB

legged_gym-master/legged_gym/envs/anymal_c/

legged_gym-master/legged_gym/envs/anymal_c/anymal.py

4.05KB

legged_gym-master/legged_gym/envs/anymal_c/flat/

legged_gym-master/legged_gym/envs/anymal_c/flat/anymal_c_flat_config.py

3.17KB

legged_gym-master/legged_gym/envs/anymal_c/mixed_terrains/

legged_gym-master/legged_gym/envs/anymal_c/mixed_terrains/anymal_c_rough_config.py

3.8KB

legged_gym-master/legged_gym/envs/base/

legged_gym-master/legged_gym/envs/base/base_config.py

2.56KB

legged_gym-master/legged_gym/envs/base/base_task.py

5.99KB

legged_gym-master/legged_gym/envs/base/legged_robot.py

47.26KB

legged_gym-master/legged_gym/envs/base/legged_robot_config.py

9.82KB

legged_gym-master/legged_gym/envs/cassie/

legged_gym-master/legged_gym/envs/cassie/cassie.py

2.05KB

legged_gym-master/legged_gym/envs/cassie/cassie_config.py

4.6KB

legged_gym-master/legged_gym/scripts/

legged_gym-master/legged_gym/scripts/play.py

5.57KB

legged_gym-master/legged_gym/scripts/train.py

2.14KB

legged_gym-master/legged_gym/tests/

legged_gym-master/legged_gym/tests/test_env.py

2.38KB

legged_gym-master/legged_gym/utils/

legged_gym-master/legged_gym/utils/__init__.py

1.85KB

legged_gym-master/legged_gym/utils/helpers.py

8.86KB

legged_gym-master/legged_gym/utils/logger.py

5.89KB

legged_gym-master/legged_gym/utils/math.py

2.36KB

legged_gym-master/legged_gym/utils/task_registry.py

7.46KB

legged_gym-master/legged_gym/utils/terrain.py

9.09KB

legged_gym-master/licenses/

legged_gym-master/licenses/assets/

legged_gym-master/licenses/assets/ANYmal_b_license.txt

1.42KB

legged_gym-master/licenses/assets/ANYmal_c_license.txt

1.44KB

legged_gym-master/licenses/assets/a1_license.txt

16.33KB

legged_gym-master/licenses/assets/cassie_license.txt

1.06KB

legged_gym-master/licenses/dependencies/

legged_gym-master/licenses/dependencies/matplotlib_license.txt

2.31KB

legged_gym-master/resources/

legged_gym-master/resources/actuator_nets/

legged_gym-master/resources/actuator_nets/anydrive_v3_lstm.pt

16.26KB

legged_gym-master/resources/robots/

legged_gym-master/resources/robots/a1/

legged_gym-master/resources/robots/a1/a1_license.txt

16.33KB

legged_gym-master/resources/robots/a1/meshes/

legged_gym-master/resources/robots/a1/meshes/calf.dae

1.68MB

legged_gym-master/resources/robots/a1/meshes/hip.dae

653.32KB

legged_gym-master/resources/robots/a1/meshes/thigh.dae

1.1MB

legged_gym-master/resources/robots/a1/meshes/thigh_mirror.dae

1.13MB

legged_gym-master/resources/robots/a1/meshes/trunk.dae

4.05MB

legged_gym-master/resources/robots/a1/meshes/trunk_A1.png

70.15KB

legged_gym-master/resources/robots/a1/urdf/

legged_gym-master/resources/robots/a1/urdf/a1.urdf

17.83KB

legged_gym-master/resources/robots/anymal_b/

legged_gym-master/resources/robots/anymal_b/ANYmal_b_license.txt

1.42KB

legged_gym-master/resources/robots/anymal_b/meshes/

legged_gym-master/resources/robots/anymal_b/meshes/anymal_base.dae

8.81MB

legged_gym-master/resources/robots/anymal_b/meshes/anymal_foot.dae

460.54KB

legged_gym-master/resources/robots/anymal_b/meshes/anymal_hip_l.dae

2.37MB

legged_gym-master/resources/robots/anymal_b/meshes/anymal_hip_r.dae

2.37MB

legged_gym-master/resources/robots/anymal_b/meshes/anymal_shank_l.dae

565.5KB

legged_gym-master/resources/robots/anymal_b/meshes/anymal_shank_r.dae

564.31KB

legged_gym-master/resources/robots/anymal_b/meshes/anymal_thigh_l.dae

2.56MB

legged_gym-master/resources/robots/anymal_b/meshes/anymal_thigh_r.dae

2.59MB

legged_gym-master/resources/robots/anymal_b/meshes/base_uv_texture.jpg

648.08KB

legged_gym-master/resources/robots/anymal_b/meshes/carbon_uv_texture.jpg

164.64KB

legged_gym-master/resources/robots/anymal_b/urdf/

legged_gym-master/resources/robots/anymal_b/urdf/anymal_b.urdf

23.81KB

legged_gym-master/resources/robots/anymal_c/

legged_gym-master/resources/robots/anymal_c/ANYmal_c_license.txt

1.44KB

legged_gym-master/resources/robots/anymal_c/meshes/

legged_gym-master/resources/robots/anymal_c/meshes/base.dae

272.28KB

legged_gym-master/resources/robots/anymal_c/meshes/base.jpg

193.8KB

legged_gym-master/resources/robots/anymal_c/meshes/battery.dae

5.21KB

legged_gym-master/resources/robots/anymal_c/meshes/battery.jpg

110.88KB

legged_gym-master/resources/robots/anymal_c/meshes/bottom_shell.dae

1.38MB

legged_gym-master/resources/robots/anymal_c/meshes/bottom_shell.jpg

165.25KB

legged_gym-master/resources/robots/anymal_c/meshes/depth_camera.dae

5.3KB

legged_gym-master/resources/robots/anymal_c/meshes/depth_camera.jpg

185.66KB

legged_gym-master/resources/robots/anymal_c/meshes/drive.dae

27.23KB

legged_gym-master/resources/robots/anymal_c/meshes/drive.jpg

173.85KB

legged_gym-master/resources/robots/anymal_c/meshes/face.dae

812.01KB

legged_gym-master/resources/robots/anymal_c/meshes/face.jpg

294.88KB

legged_gym-master/resources/robots/anymal_c/meshes/foot.dae

115.34KB

legged_gym-master/resources/robots/anymal_c/meshes/foot.jpg

185.21KB

legged_gym-master/resources/robots/anymal_c/meshes/handle.dae

22.14KB

legged_gym-master/resources/robots/anymal_c/meshes/handle.jpg

166.52KB

legged_gym-master/resources/robots/anymal_c/meshes/hatch.dae

9.43KB

legged_gym-master/resources/robots/anymal_c/meshes/hatch.jpg

102.86KB

legged_gym-master/resources/robots/anymal_c/meshes/hip.jpg

226.57KB

legged_gym-master/resources/robots/anymal_c/meshes/hip_l.dae

36.12KB

legged_gym-master/resources/robots/anymal_c/meshes/hip_r.dae

36.24KB

legged_gym-master/resources/robots/anymal_c/meshes/lidar.dae

35.54KB

legged_gym-master/resources/robots/anymal_c/meshes/lidar.jpg

180.25KB

legged_gym-master/resources/robots/anymal_c/meshes/lidar_cage.dae

65.05KB

legged_gym-master/resources/robots/anymal_c/meshes/lidar_cage.jpg

276.1KB

legged_gym-master/resources/robots/anymal_c/meshes/remote.dae

42.31KB

legged_gym-master/resources/robots/anymal_c/meshes/remote.jpg

107.39KB

legged_gym-master/resources/robots/anymal_c/meshes/shank.jpg

164.99KB

legged_gym-master/resources/robots/anymal_c/meshes/shank_l.dae

231.49KB

legged_gym-master/resources/robots/anymal_c/meshes/shank_r.dae

233.65KB

legged_gym-master/resources/robots/anymal_c/meshes/thigh.dae

259.62KB

legged_gym-master/resources/robots/anymal_c/meshes/thigh.jpg

230.57KB

legged_gym-master/resources/robots/anymal_c/meshes/top_shell.dae

1.27MB

legged_gym-master/resources/robots/anymal_c/meshes/top_shell.jpg

213.05KB

legged_gym-master/resources/robots/anymal_c/meshes/wide_angle_camera.dae

20.24KB

legged_gym-master/resources/robots/anymal_c/meshes/wide_angle_camera.jpg

152.19KB

legged_gym-master/resources/robots/anymal_c/urdf/

legged_gym-master/resources/robots/anymal_c/urdf/anymal_c.urdf

53.17KB

legged_gym-master/resources/robots/cassie/

legged_gym-master/resources/robots/cassie/cassie_license.txt

1.06KB

legged_gym-master/resources/robots/cassie/meshes/

legged_gym-master/resources/robots/cassie/meshes/abduction.stl

127.43KB

legged_gym-master/resources/robots/cassie/meshes/abduction_mirror.stl

127.82KB

legged_gym-master/resources/robots/cassie/meshes/achilles-rod.stl

15.02KB

legged_gym-master/resources/robots/cassie/meshes/hip.stl

58.04KB

legged_gym-master/resources/robots/cassie/meshes/hip_mirror.stl

58.04KB

legged_gym-master/resources/robots/cassie/meshes/knee-output.stl

66.63KB

legged_gym-master/resources/robots/cassie/meshes/knee-output_mirror.stl

66.59KB

legged_gym-master/resources/robots/cassie/meshes/pelvis.stl

177.43KB

legged_gym-master/resources/robots/cassie/meshes/plantar-rod.stl

12.58KB

legged_gym-master/resources/robots/cassie/meshes/shin-bone.stl

125.86KB

legged_gym-master/resources/robots/cassie/meshes/shin-bone_mirror.stl

126.25KB

legged_gym-master/resources/robots/cassie/meshes/tarsus.stl

112.97KB

legged_gym-master/resources/robots/cassie/meshes/tarsus_mirror.stl

112.53KB

legged_gym-master/resources/robots/cassie/meshes/thigh.stl

241.15KB

legged_gym-master/resources/robots/cassie/meshes/thigh_mirror.stl

241.15KB

legged_gym-master/resources/robots/cassie/meshes/toe-output-crank.stl

28.06KB

legged_gym-master/resources/robots/cassie/meshes/toe.stl

39.68KB

legged_gym-master/resources/robots/cassie/meshes/toe_mirror.stl

39.68KB

legged_gym-master/resources/robots/cassie/meshes/torso.stl

1.31MB

legged_gym-master/resources/robots/cassie/meshes/yaw.stl

134.46KB

legged_gym-master/resources/robots/cassie/meshes/yaw_mirror.stl

134.6KB

legged_gym-master/resources/robots/cassie/urdf/

legged_gym-master/resources/robots/cassie/urdf/cassie.urdf

16.53KB

legged_gym-master/setup.py

405B

资源内容介绍

legged-gym包

# Isaac Gym Environments for Legged Robots #This repository provides the environment used to train ANYmal (and other robots) to walk on rough terrain using NVIDIA's Isaac Gym.It includes all components needed for sim-to-real transfer: actuator network, friction & mass randomization, noisy observations and random pushes during training. **Maintainer**: Nikita Rudin **Affiliation**: Robotic Systems Lab, ETH Zurich **Contact**: rudinn@ethz.ch ---### :bell: Announcement (09.01.2024) ###With the shift from Isaac Gym to Isaac Sim at NVIDIA, we have migrated all the environments from this work to [Isaac Lab](https://github.com/isaac-sim/IsaacLab). Following this migration, this repository will receive limited updates and support. We encourage all users to migrate to the new framework for their applications.Information about this work's locomotion-related tasks in Isaac Lab is available [here](https://isaac-sim.github.io/IsaacLab/source/features/environments.html#locomotion).---### Useful Links ###Project website: https://leggedrobotics.github.io/legged_gym/ Paper: https://arxiv.org/abs/2109.11978### Installation ###1. Create a new python virtual env with python 3.6, 3.7 or 3.8 (3.8 recommended)2. Install pytorch 1.10 with cuda-11.3: - `pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html`3. Install Isaac Gym - Download and install Isaac Gym Preview 3 (Preview 2 will not work!) from https://developer.nvidia.com/isaac-gym - `cd isaacgym/python && pip install -e .` - Try running an example `cd examples && python 1080_balls_of_solitude.py` - For troubleshooting check docs `isaacgym/docs/index.html`)4. Install rsl_rl (PPO implementation) - Clone https://github.com/leggedrobotics/rsl_rl - `cd rsl_rl && git checkout v1.0.2 && pip install -e .` 5. Install legged_gym - Clone this repository - `cd legged_gym && pip install -e .`### CODE STRUCTURE ###1. Each environment is defined by an env file (`legged_robot.py`) and a config file (`legged_robot_config.py`). The config file contains two classes: one containing all the environment parameters (`LeggedRobotCfg`) and one for the training parameters (`LeggedRobotCfgPPo`). 2. Both env and config classes use inheritance. 3. Each non-zero reward scale specified in `cfg` will add a function with a corresponding name to the list of elements which will be summed to get the total reward. 4. Tasks must be registered using `task_registry.register(name, EnvClass, EnvConfig, TrainConfig)`. This is done in `envs/__init__.py`, but can also be done from outside of this repository. ### Usage ###1. Train: ```python legged_gym/scripts/train.py --task=anymal_c_flat``` - 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 `issacgym_anymal/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=anymal_c_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.### 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 `isaacgym_anymal/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.```

用户评论 (0)

发表评论

您的昵称:

评分

评论内容:

验证码:

legged-gym包

资源文件列表(大概)

资源内容介绍

用户评论 (0)

发表评论

相关资源

Dev C++ 6.7.5 red panda 调过

wow.js+animate.css

ppt截圖asddfffgsdffdfg

bisai.zip1456789