Merge pull request #218 from MyoHub/dev

2.6.0 release

.github/workflows/python-app.yml

@@ -82,7 +82,7 @@ jobs:
 #       if: ${{ runner.os == 'macOS' }}
 #       run: |
 #         brew install --cask xquartz
-#         brew install hdf5        
+#         brew install hdf5
 #         export CPATH="/opt/homebrew/include/"
 #         export HDF5_DIR=/opt/homebrew/
 #         pip3 install h5py --only-binary h5py
@@ -97,9 +97,13 @@ jobs:
       if: ${{ runner.os == 'Linux' }}
       run: python3 -m mujoco.render_test
 
-    - name: Run Test environment
+    - name: Test myoapi
       run: |
         conda activate $CONDA_DEFAULT_ENV
+        python3 -m myosuite.tests.test_myoapi
+
+    - name: Run Test environment
+      run: |
         python3 -m myosuite.tests.test_myo
 
     - name: Install ffmpeg

README.md

@@ -46,7 +46,7 @@ Test your installation using the following command (this will return also a list
 ``` bash
 python -m myosuite.tests.test_myo
 ```
- 
+
 
 You can also visualize the environments with random controls using the command below:
 ``` bash
@@ -57,7 +57,10 @@ python -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0
 mjpython -m myosuite.utils.examine_env --env_name myoElbowPose1D6MRandom-v0
 ```
 
-
+It is possible to take advantage of the latest MyoSkeleton. Once added (follow the instructions prompted by `python myosuite_init.py`), run:
+``` bash
+python myosuite/utils/examine_sim.py -s myosuite/simhive/myo_model/myoskeleton/myoskeleton.xml
+```
 
 ## Examples
 It is possible to create and interface with MyoSuite environments just like any other OpenAI gym environments. For example, to use the `myoElbowPose1D6MRandom-v0` environment, it is possible simply to run: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zFuNLsrmx42vT4oV8RbnEWtkSJ1xajEo)
@@ -74,7 +77,7 @@ for _ in range(1000):
 env.close()
 ```
 
-You can find our [tutorials](https://github.com/myohub/myosuite/tree/main/docs/source/tutorials#tutorials) on the general features and the **ICRA2023 Colab Tutorial** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KGqZgSYgKXF-vaYC33GR9llDsIW9Rp-q) **ICRA2024 Colab Tutorial** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JwxE7o6Z3bqCT4ewELacJ-Z1SV8xFhKK#scrollTo=QDppGIzHB9Zu) 
+You can find our [tutorials](https://github.com/myohub/myosuite/tree/main/docs/source/tutorials#tutorials) on the general features and the **ICRA2023 Colab Tutorial** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1KGqZgSYgKXF-vaYC33GR9llDsIW9Rp-q) **ICRA2024 Colab Tutorial** [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1JwxE7o6Z3bqCT4ewELacJ-Z1SV8xFhKK#scrollTo=QDppGIzHB9Zu)
 on how to load MyoSuite models/tasks, train them, and visualize their outcome. Also, you can find [baselines](https://github.com/myohub/myosuite/tree/main/myosuite/agents) to test some pre-trained policies.
 
 

myosuite/envs/myo/myochallenge/bimanual_v0.py

@@ -111,7 +111,7 @@ def _setup(self,
 
         # check whether the object experience force over max force
         self.over_max = False
-        self.max_force = max_force
+        self.max_force = 0
 
         self.touch_history = []
 
@@ -177,7 +177,8 @@ def get_obs_dict(self, sim):
 
         current_force = sim.data.sensordata[0]
         if current_force > self.max_force:
-            self.over_max = True
+            self.max_force = current_force
+        obs_dict['max_force'] = np.array([self.max_force])
 
         obs_vec = self._obj_label_to_obs(touching_objects)
         obs_dict["touching_body"] = obs_vec
@@ -236,7 +237,8 @@ def get_reward_dict(self, obs_dict):
                 ("pass_err", pass_dist + np.log(pass_dist + 1e-3)),
                 # Must keys
                 ("sparse", 0),
-                ("solved", self.check_solve(goal_dis)),
+                ("goal_dist", goal_dis), 
+                ("solved", goal_dis < self.proximity_th),
                 ("done", False),
             )
         )
@@ -258,12 +260,6 @@ def step(self, a, **kwargs):
                                                       - self.sim.model.actuator_ctrlrange[robotic_act_ind, 0]) / 2.0)
         return super().step(processed_controls, **kwargs)
 
-    def check_solve(self, goal_dis):
-        if goal_dis > 0.01:
-            return False
-        if self.over_max == True:
-            return False
-        return True
 
     def get_metrics(self, paths, successful_steps=5):
         """
@@ -280,15 +276,19 @@ def get_metrics(self, paths, successful_steps=5):
                 num_success += 1
         score = num_success / num_paths
 
-        times = np.mean([np.round(p['env_infos']['obs_dict']['time'][-1], 2) for p in paths])
+        times = np.mean([np.round(p['env_infos']['obs_dict']['time'][-1], 5) for p in paths])
+        max_force = np.mean([np.round(p['env_infos']['obs_dict']['max_force'][-1], 5) for p in paths])
+        goal_dist = np.mean([np.mean(p['env_infos']['rwd_dict']['goal_dist']) for p in paths])
 
         # average activations over entire trajectory (can be shorter than horizon, if done) realized
-        effort = -1.0 * np.mean([np.mean(p['env_infos']['rwd_dict']['act_reg']) for p in paths])
+        effort = -1.0 * np.mean([np.mean(p['env_infos']['rwd_dict']['act']) for p in paths])
 
         metrics = {
             'score': score,
             'time': times,
             'effort': effort,
+            'peak force': max_force,
+            'goal dist': goal_dist, 
         }
         return metrics
 

myosuite/envs/obs_vec_dict.py

@@ -78,10 +78,10 @@ def obsdict2obsvec(self, obs_dict, ordered_obs_keys):
         if not self.initialized:
             self.initialize(obs_dict, ordered_obs_keys)
 
-        # recover vec
-        obsvec = np.zeros(0)
+        obs_list = [np.zeros(0)]
         for key in self.ordered_obs_keys:
-            obsvec = np.concatenate([obsvec, obs_dict[key].ravel()]) # ravel helps with images
+            obs_list.append(obs_dict[key].ravel())  # ravel helps with images
+        obsvec = np.concatenate(obs_list, dtype=np.float32)
 
         # cache
         t = obs_dict['time']

myosuite/tests/test_myoapi.py

@@ -0,0 +1,25 @@
+
+from pathlib import Path
+from unittest.mock import patch
+
+from myosuite_init import fetch_simhive
+
+my_file = Path("/path/to/file")
+
+@patch('builtins.input', side_effect=['no'])
+def test_no_myoapi(mock_input):
+    print("mock_input", mock_input.side_effect)
+    fetch_simhive()
+    assert not Path("myosuite/simhive/myo_model/myoskeleton/myoskeleton.xml").exists()
+
+@patch('builtins.input', side_effect=['yes'])
+def test_yes_myoapi(mock_input):
+    print("mock_input", mock_input.side_effect)
+    fetch_simhive()
+    assert Path("myosuite/simhive/myo_model/myoskeleton/myoskeleton.xml").exists()
+
+test_no_myoapi()
+test_yes_myoapi()
+
+
+

myosuite/version.py

@@ -1 +1 @@
-__version_tuple__ = (2, 5, 0)
+__version_tuple__ = (2, 6, 0)

myosuite_init.py

@@ -0,0 +1,113 @@
+import os
+import shutil
+from os.path import expanduser
+
+import git
+
+curr_dir = os.path.dirname(os.path.abspath(__file__))
+simhive_path = os.path.join(curr_dir, 'myosuite', 'simhive')
+
+
+# from myosuite.utils.import_utils import fetch_git
+
+def fetch_git(repo_url, commit_hash, clone_directory, clone_path=None):
+    """
+    fetch git repo using provided details
+    """
+    if clone_path is None:
+        clone_path = os.path.join(expanduser("~"), ".myosuite")
+    clone_directory = os.path.join(clone_path, clone_directory)
+
+    try:
+        # Create the clone directory if it doesn't exist
+        os.makedirs(clone_directory, exist_ok=True)
+
+        # Clone the repository to the specified path
+        if not os.path.exists(os.path.join(clone_directory,'.git')):
+            repo = git.Repo.clone_from(repo_url, clone_directory)
+            print(f"{repo_url} cloned at {clone_directory}")
+        else:
+            repo = git.Repo(clone_directory)
+            origin = repo.remote('origin')
+            origin.fetch()
+
+        # Check out the specific commit if not already
+        current_commit_hash = repo.head.commit.hexsha
+        if current_commit_hash != commit_hash:
+            repo.git.checkout(commit_hash)
+            print(f"{repo_url}@{commit_hash} fetched at {clone_directory}")
+    except git.GitCommandError as e:
+        print(f"Error: {e}")
+
+    return clone_directory
+
+
+def clean_simhive():
+    """
+    Remove cached simhive if it exists
+    """
+    print("MyoSuite:> Clearing SimHive ...")
+    api_path = os.path.join(simhive_path, 'myo_model')
+    if os.path.exists(api_path):
+        shutil.rmtree(api_path)
+    else:
+        print("MyoSuite:> SimHive/myo_model directory does not exist.")
+    print("MyoSuite:> SimHive cleared")
+
+
+def accept_license():
+    prompt = """
+A permissive license for non-commercial scientific research is available.
+You can review the license at: https://github.com/myolab/myo_model/blob/main/LICENSE
+
+Do you accept the terms of the license? (yes/no):
+"""
+    response = input(prompt).strip().lower()
+
+    if response == 'yes':
+        print("Thank you for accepting the license. You may proceed.")
+        return True
+    elif response == 'no':
+        print("You have rejected the license terms. Exiting...")
+        return False
+    else:
+        print("Invalid input. Please enter 'yes' or 'no'.")
+        return accept_license()  # Recursively prompt again for valid input
+
+
+def fetch_simhive():
+    """
+    fetch a copy of simhive
+    """
+    print("MyoSuite:> Initializing...")
+
+    # Mark the SimHive version (ToDo: Remove this when commits hashes are auto fetched from submodules)
+    __version__ = "2.5.0"
+
+    # Inform user about API
+    if accept_license():
+        # Proceed with the rest of the code
+        print("MyoSuite:> License accepted. Proceeding initialization ...")
+    else:
+        # Exit or handle the rejection case
+        print("MyoSuite:> License rejected. Exiting")
+        return
+
+    # Fetch SimHive
+    print("MyoSuite:> Downloading simulation assets (upto ~100MBs)")
+    fetch_git(repo_url="https://github.com/myolab/myo_model.git",
+                commit_hash="619b1a876113e91a302b9baeaad6c2341e12ac81",
+                clone_directory="myo_model",
+                clone_path=simhive_path)
+
+
+    # mark successful creation of simhive
+    filename = os.path.join(simhive_path, "simhive-version")
+    with open(filename, 'w') as file:
+        file.write(__version__)
+
+    print("MyoSuite:> Successfully Initialized.")
+
+
+if __name__ == "__main__":
+    fetch_simhive()

setup.py

@@ -56,4 +56,10 @@ def package_files(directory):
         packages=find_packages(exclude=("myosuite.agents")),
         python_requires=">=3.8",
         install_requires=fetch_requirements(),
+        entry_points={
+            'console_scripts': [
+                'myoapi_init = myosuite_init:fetch_simhive',
+                'myoapi_clean = myosuite_init:clean_simhive',
+            ],
+        },
     )