Should be good

Author: AntoineRichard

scripts/environments/export_IODescriptors.py

@@ -49,8 +49,9 @@ def main():
     env.reset()
 
     outs = env.unwrapped.get_IO_descriptors
-    out = outs["observations"]
+    out_observations = outs["observations"]
     out_actions = outs["actions"]
+    out_articulations = outs["articulations"]
     # Make a yaml file with the output
     import yaml
 
@@ -60,15 +61,25 @@ def main():
     with open(f"{name}_IO_descriptors.yaml", "w") as f:
         yaml.safe_dump(outs, f)
 
-    for k, v in out_actions.items():
-        print(f"--- Action term: {k} ---")
-        for k1, v1 in v.items():
+    for k in out_actions:
+        print(f"--- Action term: {k['name']} ---")
+        k.pop("name")
+        for k1, v1 in k.items():
             print(f"{k1}: {v1}")
 
-    for k, v in out.items():
-        print(f"--- Obs term: {k} ---")
-        for k1, v1 in v.items():
+    for obs_group_name, obs_group in out_observations.items():
+        print(f"--- Obs group: {obs_group_name} ---")
+        for k in obs_group:
+            print(f"--- Obs term: {k['name']} ---")
+            k.pop("name")
+            for k1, v1 in k.items():
+                print(f"{k1}: {v1}")
+
+    for articulation_name, articulation_data in out_articulations.items():
+        print(f"--- Articulation: {articulation_name} ---")
+        for k1, v1 in articulation_data.items():
             print(f"{k1}: {v1}")
+
     env.step(torch.zeros(env.action_space.shape, device=env.unwrapped.device))
     env.close()
 

source/isaaclab/isaaclab/envs/manager_based_env.py

@@ -21,7 +21,7 @@
 from .common import VecEnvObs
 from .manager_based_env_cfg import ManagerBasedEnvCfg
 from .ui import ViewportCameraController
-
+from .utils.io_descriptors import export_articulations_data
 
 class ManagerBasedEnv:
     """The base environment encapsulates the simulation scene and the environment managers for the manager-based workflow.
@@ -220,6 +220,7 @@ def get_IO_descriptors(self):
         return {
             "observations": self.observation_manager.get_IO_descriptors,
             "actions": self.action_manager.get_IO_descriptors,
+            "articulations": export_articulations_data(self),
         }
 
     """

source/isaaclab/isaaclab/envs/mdp/actions/joint_actions.py

@@ -138,8 +138,11 @@ def IO_descriptor(self) -> GenericActionIODescriptor:
         else:
             self._IO_descriptor.offset = self._offset
         #FIXME: This is not correct. Add list support.
-        if isinstance(self._clip, torch.Tensor):
-            self._IO_descriptor.clip = self._clip[0].detach().cpu().numpy().tolist()
+        if self.cfg.clip is not None:
+            if isinstance(self._clip, torch.Tensor):
+                self._IO_descriptor.clip = self._clip[0].detach().cpu().numpy().tolist()
+            else:
+                self._IO_descriptor.clip = self._clip
         else:
             self._IO_descriptor.clip = None
         return self._IO_descriptor

source/isaaclab/isaaclab/managers/action_manager.py

@@ -271,7 +271,7 @@ def has_debug_vis_implementation(self) -> bool:
         return has_debug_vis
 
     @property
-    def get_IO_descriptors(self) -> dict[str, dict[str, Any]]:
+    def get_IO_descriptors(self) -> list[dict[str, Any]]:
         """Get the IO descriptors for the action manager.
 
         Returns:
@@ -286,10 +286,10 @@ def get_IO_descriptors(self) -> dict[str, dict[str, Any]]:
             except Exception as e:
                 print(f"Error getting IO descriptor for term '{term_name}': {e}")
 
-        formatted_data = {}
+        formatted_data = []
         for item in data:
             name = item.pop("name")
-            formatted_item = {"extras": item.pop("extras")}
+            formatted_item = {"name": name, "extras": item.pop("extras")}
             for k, v in item.items():
                 # Check if v is a tuple and convert to list
                 if isinstance(v, tuple):
@@ -298,7 +298,7 @@ def get_IO_descriptors(self) -> dict[str, dict[str, Any]]:
                     formatted_item["extras"][k] = v
                 else:
                     formatted_item[k] = v
-            formatted_data[name] = formatted_item
+            formatted_data.append(formatted_item)
 
         return formatted_data
 

source/isaaclab/isaaclab/managers/observation_manager.py

@@ -226,7 +226,7 @@ def group_obs_concatenate(self) -> dict[str, bool]:
         return self._group_obs_concatenate
 
     @property
-    def get_IO_descriptors(self):
+    def get_IO_descriptors(self, group_names_to_export: list[str] = ["policy"]):
         """Get the IO descriptors for the observation manager.
 
         Returns:
@@ -276,13 +276,17 @@ def get_IO_descriptors(self):
                     # Check if v is a tuple and convert to list
                     if isinstance(v, tuple):
                         v = list(v)
+                    # Check if v is a tensor and convert to list
+                    if isinstance(v, torch.Tensor):
+                        v = v.detach().cpu().numpy().tolist()
                     if k in ["scale", "clip", "history_length", "flatten_history_dim"]:
                         formatted_item["overloads"][k] = v
                     elif k in ["modifiers", "description", "units"]:
                         formatted_item["extras"][k] = v
                     else:
                         formatted_item[k] = v
                 formatted_data[group_name].append(formatted_item)
+        formatted_data = {k: v for k, v in formatted_data.items() if k in group_names_to_export}
         return formatted_data
 
     """