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README.md

@@ -11,8 +11,11 @@ tags:
   - industrial
   - tool-wear
   - cnc
+  - franka-panda
+  - assembly
+  - manipulation
 size_categories:
-  - 1M<n<10M
+  - 10M<n<100M
 language:
   - en
 pretty_name: FactoryNet Hackathon Dataset
@@ -31,8 +34,10 @@ FactoryNet unifies multiple industrial operation datasets into a common schema f
 | **AURSAD** | UR3e (6-DOF cobot) | Screwdriving | 4,094 | 6.2M | 5 types |
 | **voraus-AD** | Yu-Cobot (6-DOF) | Pick-and-place | 2,122 | 2.3M | 12 types |
 | **NASA Milling** | CNC (3-axis) | Milling | 167 | 1.5M | Tool wear |
+| **RH20T** | Franka Panda (7-DOF) | Manipulation | 500 | 1.2M | Normal |
+| **REASSEMBLE** | Franka Panda (7-DOF) | Assembly | 100 | 1.0M | Task success/fail |
 
-**Total: 6,383 episodes, 10M+ rows**
+**Total: 6,983 episodes, 12M+ rows**
 
 ## FactoryNet Schema
 
@@ -81,6 +86,16 @@ print(f"voraus-AD: {df['episode_id'].nunique()} episodes")
 ds = load_dataset("Forgis/factorynet-hackathon", data_dir="nasa_milling")
 df = ds['train'].to_pandas()
 print(f"NASA Milling: {df['episode_id'].nunique()} episodes")
+
+# Load RH20T (Franka manipulation tasks)
+ds = load_dataset("Forgis/factorynet-hackathon", data_dir="rh20t")
+df = ds['train'].to_pandas()
+print(f"RH20T: {df['episode_id'].nunique()} episodes")
+
+# Load REASSEMBLE (Franka assembly tasks)
+ds = load_dataset("Forgis/factorynet-hackathon", data_dir="reassemble")
+df = ds['train'].to_pandas()
+print(f"REASSEMBLE: {df['episode_id'].nunique()} episodes")
 ```
 
 ## Minimum Viable Episode (MVE)
@@ -101,23 +116,28 @@ This enables causal analysis: if `effort` doesn't follow `setpoint`, something i
 | `grip_failure` | Gripper malfunction | voraus-AD |
 | `missing_part` | Expected part absent | AURSAD |
 | `tool_wear` | Progressive degradation | NASA Milling |
+| `task_failure` | Assembly step failed | REASSEMBLE |
 
 ## File Structure
 
 ```
 Forgis/factorynet-hackathon/
 ├── aursad/
-│   └── aursad_factorynet.parquet        # 1.5 GB - UR3e time series
+│   └── aursad_factorynet.parquet           # 1.5 GB - UR3e time series
 ├── voraus/
 │   └── voraus_ad_100hz_factorynet.parquet  # 461 MB - Yu-Cobot time series
 ├── nasa_milling/
-│   ├── nasa_milling_factorynet.parquet  # 15 MB - CNC time series
-│   └── nasa_milling_metadata.json       # Episode metadata with wear labels
-├── metadata/
-│   ├── aursad_metadata.json             # 78 MB - AURSAD episode metadata
-│   └── voraus_metadata.json             # 53 MB - voraus episode metadata
-├── schema.json                          # FactoryNet schema reference
-├── factorynet_loader.py                 # Easy-load Python utility
+│   ├── nasa_milling_factorynet.parquet     # 15 MB - CNC time series
+│   └── nasa_milling_metadata.json          # Episode metadata with wear labels
+├── rh20t/
+│   ├── rh20t_factorynet.parquet            # 31 MB - Franka manipulation
+│   └── rh20t_metadata.json                 # Episode metadata
+├── reassemble/
+│   ├── reassemble_factorynet.parquet       # 406 MB - Franka assembly
+│   ├── reassemble_extensions.parquet       # 19 MB - Gripper data
+│   └── reassemble_metadata.json            # Episode + segment metadata
+├── schema.json                             # FactoryNet schema reference
+├── factorynet_loader.py                    # Easy-load Python utility
 └── README.md
 ```
 
@@ -149,6 +169,8 @@ This dataset unifies and standardizes:
 - **AURSAD**: [Zenodo](https://zenodo.org/records/4487073) - CC BY 4.0
 - **voraus-AD**: [GitHub](https://github.com/vorausrobotik/voraus-ad-dataset) - MIT License
 - **NASA Milling**: [NASA Open Data](https://data.nasa.gov/dataset/milling-wear) - Public Domain
+- **RH20T**: [Project Page](https://rh20t.github.io/) - Real Human-Robot 20 Tasks
+- **REASSEMBLE**: [GitHub](https://github.com/TUWIEN-ASL/REASSEMBLE) - Assembly/Disassembly Dataset
 
 ## License