Enable metrics in policy_runner script (#103)

## Summary
Policy runner reports metrics results depending on the task

## Detailed description
- Fix data_class assertion when importing path differs
- Add compute_metric() in to policy_runner.py

Author: xyao-nv

isaac_arena/examples/policy_runner.py

@@ -38,13 +38,19 @@ def main():
         # app. Given current SimulationAppContext setup, use lazy import to handle policy-related
         # deps inside create_policy() function to bringup sim app.
         policy, num_steps = create_policy(args_cli)
+        # NOTE(xinjieyao, 2025-10-07): lazy import to prevent app stalling caused by omni.kit
+        from isaac_arena.metrics.metrics import compute_metrics
 
         for _ in tqdm.tqdm(range(num_steps)):
             with torch.inference_mode():
                 actions = policy.get_action(env, obs)
                 obs, _, terminated, truncated, _ = env.step(actions)
                 if terminated.any() or truncated.any():
                     obs, _ = env.reset()
+
+        metrics = compute_metrics(env)
+        print(f"Metrics: {metrics}")
+
         # Close the environment.
         env.close()
 

isaac_arena/metrics/metrics.py

@@ -87,7 +87,12 @@ def get_metric_recorder_dataset_path(env: ManagerBasedRLEnv) -> pathlib.Path:
     Returns:
         The path to the dataset for the metric recorder.
     """
-    assert env.cfg.recorders.dataset_file_handler_class_type == HDF5DatasetFileHandler
+    # Check if the dataset file handler is HDF5DatasetFileHandler
+    # Use class name comparison instead of direct equality to handle different import contexts
+    handler_class = env.cfg.recorders.dataset_file_handler_class_type
+    assert (
+        handler_class.__name__ == HDF5DatasetFileHandler.__name__
+    ), f"Expected HDF5DatasetFileHandler, got {handler_class.__name__}"
     return pathlib.Path(env.cfg.recorders.dataset_export_dir_path) / pathlib.Path(
         env.cfg.recorders.dataset_filename + ".hdf5"
     )

isaac_arena/metrics/success_rate.py

@@ -77,6 +77,8 @@ def compute_metric_from_recording(self, recorded_metric_data: list[np.ndarray])
                 which the environment was successful.
         """
         num_demos = len(recorded_metric_data)
+        if num_demos == 0:
+            return 0.0
         all_demos_success_flags = np.concatenate(recorded_metric_data)
         assert all_demos_success_flags.ndim == 1
         assert all_demos_success_flags.shape[0] == num_demos