diff --git a/libero2lerobot/libero_h5.py b/libero2lerobot/libero_h5.py
index 4ea2df1..f7d10cd 100644
--- a/libero2lerobot/libero_h5.py
+++ b/libero2lerobot/libero_h5.py
@@ -4,6 +4,7 @@ import re
 import shutil
 from pathlib import Path
 
+import numpy as np
 import pandas as pd
 import ray
 from datatrove.executor import LocalPipelineExecutor, RayPipelineExecutor
@@ -115,30 +116,45 @@ class AggregateDatasets(PipelineStep):
 
             datasets_task_index_to_aggr_task_index[dataset_index] = task_index_to_aggr_task_index
 
-        datasets_ep_idx_to_aggr_ep_idx = {}
         datasets_aggr_episode_index_shift = {}
         datasets_aggr_index_shift = {}
-        aggr_episode_index_shift = 0
         for dataset_index, meta in enumerate(tqdm(all_metadata, desc="Aggregate episodes and global index")):
-            ep_idx_to_aggr_ep_idx = {}
-
-            for episode_index in range(meta.total_episodes):
-                aggr_episode_index = episode_index + aggr_episode_index_shift
-                ep_idx_to_aggr_ep_idx[episode_index] = aggr_episode_index
-
-            datasets_ep_idx_to_aggr_ep_idx[dataset_index] = ep_idx_to_aggr_ep_idx
-            datasets_aggr_episode_index_shift[dataset_index] = aggr_episode_index_shift
+            datasets_aggr_episode_index_shift[dataset_index] = aggr_meta.total_episodes
             datasets_aggr_index_shift[dataset_index] = aggr_meta.total_frames
 
             # populate episodes
             for episode_index, episode_dict in meta.episodes.items():
-                aggr_episode_index = episode_index + aggr_episode_index_shift
+                aggr_episode_index = episode_index + aggr_meta.total_episodes
                 episode_dict["episode_index"] = aggr_episode_index
                 aggr_meta.episodes[aggr_episode_index] = episode_dict
 
             # populate episodes_stats
             for episode_index, episode_stats in meta.episodes_stats.items():
-                aggr_episode_index = episode_index + aggr_episode_index_shift
+                aggr_episode_index = episode_index + aggr_meta.total_episodes
+                episode_stats["index"].update(
+                    {
+                        "min": episode_stats["index"]["min"] + aggr_meta.total_frames,
+                        "max": episode_stats["index"]["max"] + aggr_meta.total_frames,
+                        "mean": episode_stats["index"]["mean"] + aggr_meta.total_frames,
+                    }
+                )
+                episode_stats["episode_index"].update(
+                    {
+                        "min": np.array([aggr_episode_index]),
+                        "max": np.array([aggr_episode_index]),
+                        "mean": np.array([aggr_episode_index]),
+                    }
+                )
+                df = pd.read_parquet(meta.root / meta.get_data_file_path(episode_index))
+                df["task_index"] = df["task_index"].map(datasets_task_index_to_aggr_task_index[dataset_index])
+                episode_stats["task_index"].update(
+                    {
+                        "min": np.array([df["task_index"].min()]),
+                        "max": np.array([df["task_index"].max()]),
+                        "mean": np.array([df["task_index"].mean()]),
+                        "std": np.array([df["task_index"].std()]),
+                    }
+                )
                 aggr_meta.episodes_stats[aggr_episode_index] = episode_stats
 
             # populate info
@@ -146,11 +162,9 @@ class AggregateDatasets(PipelineStep):
             aggr_meta.info["total_frames"] += meta.total_frames
             aggr_meta.info["total_videos"] += len(aggr_meta.video_keys) * meta.total_episodes
 
-            aggr_episode_index_shift += meta.total_episodes
-
         logger.info("Write meta data")
         aggr_meta.info["total_tasks"] = len(aggr_meta.tasks)
-        aggr_meta.info["total_chunks"] = aggr_meta.get_episode_chunk(aggr_episode_index_shift - 1)
+        aggr_meta.info["total_chunks"] = aggr_meta.get_episode_chunk(aggr_meta.total_episodes - 1) + 1
         aggr_meta.info["splits"] = {"train": f"0:{aggr_meta.info['total_episodes']}"}
 
         # create a new episodes jsonl with updated episode_index using write_episode
@@ -168,7 +182,6 @@ class AggregateDatasets(PipelineStep):
         write_info(aggr_meta.info, aggr_meta.root)
 
         self.datasets_task_index_to_aggr_task_index = datasets_task_index_to_aggr_task_index
-        self.datasets_ep_idx_to_aggr_ep_idx = datasets_ep_idx_to_aggr_ep_idx
         self.datasets_aggr_episode_index_shift = datasets_aggr_episode_index_shift
         self.datasets_aggr_index_shift = datasets_aggr_index_shift
 
@@ -187,7 +200,7 @@ class AggregateDatasets(PipelineStep):
         with self.track_time("aggregating dataset"):
             logger.info("Copy data")
             for episode_index in range(meta.total_episodes):
-                aggr_episode_index = self.datasets_ep_idx_to_aggr_ep_idx[dataset_index][episode_index]
+                aggr_episode_index = episode_index + aggr_episode_index_shift
                 data_path = meta.root / meta.get_data_file_path(episode_index)
                 aggr_data_path = aggr_meta.root / aggr_meta.get_data_file_path(aggr_episode_index)
                 aggr_data_path.parent.mkdir(parents=True, exist_ok=True)
@@ -258,6 +271,7 @@ def main(
         aggregate_output_path = output_path / ("_".join([src_path.name for src_path in src_paths]) + "_aggregated_lerobot")
     else:
         aggregate_output_path = output_path / f"{src_paths[0].name}_lerobot"
+    aggregate_output_path = aggregate_output_path.resolve()
 
     if debug:
         executor = "local"