Update README.md

README.md

@@ -4,18 +4,146 @@ task_categories:
   - robotics
 tags:
   - LeRobot
+  - UMI
+  - robotics
+  - imitation-learning
+  
+configs:
+  - config_name: task_001_press_the_power_button_of_the_power_strip
+    default: true
+    data_files:
+      - split: train
+        path: task_001_press_the_power_button_of_the_power_strip/data/*/*.parquet
+  - config_name: task_002_transfer_the_calculator_and_then_place_it_on_the_table_to_align
+    data_files:
+      - split: train
+        path: task_002_transfer_the_calculator_and_then_place_it_on_the_table_to_align/data/*/*.parquet
+  - config_name: task_003_the_plug_is_inserted_into_the_power_strip
+    data_files:
+      - split: train
+        path: task_003_the_plug_is_inserted_into_the_power_strip/data/*/*.parquet
+  - config_name: task_004_unfold_the_cleaning_cloth_and_wipe_the_table
+    data_files:
+      - split: train
+        path: task_004_unfold_the_cleaning_cloth_and_wipe_the_table/data/*/*.parquet
+  - config_name: task_005_scaffold_handover_and_alignment
+    data_files:
+      - split: train
+        path: task_005_scaffold_handover_and_alignment/data/*/*.parquet
+  - config_name: task_006_throw_the_napkin_in_the_trash_can
+    data_files:
+      - split: train
+        path: task_006_throw_the_napkin_in_the_trash_can/data/*/*.parquet
+  - config_name: task_007_put_the_usb_flash_drive_in_the_storage_cabinet
+    data_files:
+      - split: train
+        path: task_007_put_the_usb_flash_drive_in_the_storage_cabinet/data/*/*.parquet
+  - config_name: task_008_clean_the_sponge_and_put_it_into_the_basin
+    data_files:
+      - split: train
+        path: task_008_clean_the_sponge_and_put_it_into_the_basin/data/*/*.parquet
+  - config_name: task_009_throw_away_the_snack_packaging_wipe_the_table
+    data_files:
+      - split: train
+        path: task_009_throw_away_the_snack_packaging_wipe_the_table/data/*/*.parquet
+  - config_name: task_010_put_tableware_into_the_chopstick_cage
+    data_files:
+      - split: train
+        path: task_010_put_tableware_into_the_chopstick_cage/data/*/*.parquet
+  - config_name: task_011_put_fruits_into_the_freshkeeping_box
+    data_files:
+      - split: train
+        path: task_011_put_fruits_into_the_freshkeeping_box/data/*/*.parquet
+  - config_name: task_012_put_the_mat_on_the_table_and_then_put_the_cup_on_the_mat
+    data_files:
+      - split: train
+        path: task_012_put_the_mat_on_the_table_and_then_put_the_cup_on_the_mat/data/*/*.parquet
+  - config_name: task_013_clean_the_cup_with_a_brush
+    data_files:
+      - split: train
+        path: task_013_clean_the_cup_with_a_brush/data/*/*.parquet
+  - config_name: task_014_open_the_lid_and_add_water_to_the_rice_cooker
+    data_files:
+      - split: train
+        path: task_014_open_the_lid_and_add_water_to_the_rice_cooker/data/*/*.parquet
+  - config_name: task_015_put_the_measuring_cup_and_wooden_spoon_on_the_tray
+    data_files:
+      - split: train
+        path: task_015_put_the_measuring_cup_and_wooden_spoon_on_the_tray/data/*/*.parquet
+  - config_name: task_016_wipe_the_countertop_and_place_the_small_pot
+    data_files:
+      - split: train
+        path: task_016_wipe_the_countertop_and_place_the_small_pot/data/*/*.parquet
+  - config_name: task_017_put_the_carrot_on_the_board
+    data_files:
+      - split: train
+        path: task_017_put_the_carrot_on_the_board/data/*/*.parquet
+  - config_name: task_018_put_the_lid_on_the_cup
+    data_files:
+      - split: train
+        path: task_018_put_the_lid_on_the_cup/data/*/*.parquet
+  - config_name: task_019_open_the_drawer_clip_the_eraser_and_put_it_in_the_drawer
+    data_files:
+      - split: train
+        path: task_019_open_the_drawer_clip_the_eraser_and_put_it_in_the_drawer/data/*/*.parquet
+  - config_name: task_020_open_the_drawer_clip_the_stapler_and_put_it_in_the_drawer
+    data_files:
+      - split: train
+        path: task_020_open_the_drawer_clip_the_stapler_and_put_it_in_the_drawer/data/*/*.parquet
+  - config_name: task_021_put_the_ham_sausage_into_the_freshkeeping_box
+    data_files:
+      - split: train
+        path: task_021_put_the_ham_sausage_into_the_freshkeeping_box/data/*/*.parquet
+  - config_name: task_022_the_spatula_goes_into_the_drawer
+    data_files:
+      - split: train
+        path: task_022_the_spatula_goes_into_the_drawer/data/*/*.parquet
+  - config_name: task_023_pick_up_the_object_and_put_it_into_the_sock_storage_box
+    data_files:
+      - split: train
+        path: task_023_pick_up_the_object_and_put_it_into_the_sock_storage_box/data/*/*.parquet
+  - config_name: task_024_pick_up_objects_and_put_them_into_the_sock_storage_box
+    data_files:
+      - split: train
+        path: task_024_pick_up_objects_and_put_them_into_the_sock_storage_box/data/*/*.parquet
+  - config_name: task_025_one_hand_picks_up_the_pen_and_the_other_hand_takes_off_the_cap
+    data_files:
+      - split: train
+        path: task_025_one_hand_picks_up_the_pen_and_the_other_hand_takes_off_the_cap/data/*/*.parquet
+  - config_name: task_026_pick_up_the_vase_and_insert_flowers
+    data_files:
+      - split: train
+        path: task_026_pick_up_the_vase_and_insert_flowers/data/*/*.parquet
+  - config_name: task_027_put_the_items_into_the_small_pillow_storage_box
+    data_files:
+      - split: train
+        path: task_027_put_the_items_into_the_small_pillow_storage_box/data/*/*.parquet
+  - config_name: task_028_folding_square_towels
+    data_files:
+      - split: train
+        path: task_028_folding_square_towels/data/*/*.parquet
+  - config_name: task_029_put_baby_products_into_the_sterilizer
+    data_files:
+      - split: train
+        path: task_029_put_baby_products_into_the_sterilizer/data/*/*.parquet
+  - config_name: task_030_put_the_baby_shoes_into_the_container
+    data_files:
+      - split: train
+        path: task_030_put_the_baby_shoes_into_the_container/data/*/*.parquet
 ---
+
 <div align="center">
 
 # UMI Open 5000
 
-### Large-scale Robotic Manipulation Dataset for UMI / FastUMI-style Learning
+### Verified UMI Trajectories for High-Fidelity Replay
 
-#### LeRobot v3.0 · `fastumi` · 30 tasks · **5,000 episodes** total
+#### Adapted to RealMan, Acone, and R1Pro Embodiments · LeRobot v3.0 · 30 Tasks · **5,000 Episodes**
 
 ![Dataset](https://img.shields.io/badge/Dataset-UMI%20Open%205000-blue)
+![Verified](https://img.shields.io/badge/Verified-Replay%20Tested-brightgreen)
+![Embodiments](https://img.shields.io/badge/Embodiments-RealMan%20%7C%20Acone%20%7C%20R1Pro-orange)
 ![LeRobot](https://img.shields.io/badge/LeRobot-v3.0-green)
-![Tasks](https://img.shields.io/badge/Tasks-30-orange)
 ![Episodes](https://img.shields.io/badge/Episodes-5000-purple)
 ![License](https://img.shields.io/badge/License-Apache--2.0-lightgrey)
 
@@ -42,9 +170,26 @@ tags:
 
 ## Overview
 
-**UMI Open 5000** is a robotic manipulation dataset containing **5,000 episodes** across **30 household and tabletop manipulation tasks**.
+**UMI Open 5000** is a verified robotic manipulation dataset containing **5,000 UMI trajectories** across **30 household and tabletop manipulation tasks**.
+
+The trajectories have been checked for replay readiness and are adapted to three representative robot embodiments:
+
+- **RealMan**
+- **Acone**
+- **R1Pro**
+
+The dataset is designed for **high-fidelity trajectory replay**, imitation learning, visuomotor policy learning, and cross-embodiment robotic manipulation research. It is organized in the **LeRobot v3.0** format for convenient loading, training, and evaluation.
+
+## Key Features
 
-The dataset is organized in the **LeRobot v3.0** format and is intended for research on imitation learning, visuomotor policy learning, and general-purpose robotic manipulation.
+| Feature | Description |
+|--------|-------------|
+| Verified UMI trajectories | The dataset consists of checked UMI trajectories prepared for reliable downstream use. |
+| Replay-tested data | Trajectories are intended for high-quality replay rather than offline-only inspection. |
+| Multi-embodiment compatibility | Adapted to RealMan, Acone, and R1Pro robot configurations. |
+| LeRobot v3.0 format | Organized in a standard format for loading, training, and evaluation. |
+| 30 manipulation tasks | Covers household, tabletop, tool-use, cleaning, storage, and object-transfer scenarios. |
+| 5,000 episodes | Provides a compact but diverse benchmark for robotic manipulation research. |
 
 ## Dataset Summary
 
@@ -52,9 +197,11 @@ The dataset is organized in the **LeRobot v3.0** format and is intended for rese
 |------|-------|
 | Dataset name | UMI Open 5000 |
 | Format | LeRobot v3.0 |
-| Modality | Robot trajectories |
+| Trajectory type | UMI trajectories |
 | Number of tasks | 30 |
 | Number of episodes | 5,000 |
+| Target embodiments | RealMan, Acone, R1Pro |
+| Primary use cases | Trajectory replay, imitation learning, visuomotor policy learning |
 | License | Apache-2.0 |
 
 ## Task List
@@ -95,13 +242,12 @@ The dataset is organized in the **LeRobot v3.0** format and is intended for rese
 
 ## Loading the Dataset
 
-Load a specific task configuration:
+Load the default task:
 
 ```python
 from datasets import load_dataset
 
 dataset = load_dataset(
     "your-username/UMI-Open-5000",
-    "task_001_press_the_power_button_of_the_power_strip",
     split="train",
 )