Add files using upload-large-folder tool

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+fastumi_test/1.0.0/fastumi_test-train.tfrecord-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,92 @@
+# fastumi_test
+
+## Description
+FastUMI Pro robot manipulation dataset in RLDS format.
+
+**Task**: pick up the object
+
+## Dataset Info
+- **Format**: RLDS (Reinforcement Learning Datasets)
+- **Total Episodes**: 12
+- **Total Steps**: 6692
+- **FPS**: 30
+- **Robot Type**: fastumi_pro
+- **Action Type**: absolute
+- **Image Encoding**: JPEG
+- **Image Shape**: (1080, 1920, 3)
+
+## Features
+
+### Observation
+| Feature | Shape | Description |
+|---------|-------|-------------|
+| `observation/image` | (1080, 1920, 3) | RGB camera image |
+| `observation/state` | (7,) | End-effector pose: [x, y, z, roll, pitch, yaw, gripper] |
+
+### Action
+| Feature | Shape | Description |
+|---------|-------|-------------|
+| `action` | (7,) | Target pose (next state) |
+
+### Standard RLDS Fields
+| Feature | Type | Description |
+|---------|------|-------------|
+| `reward` | float | 1.0 at episode end, 0.0 otherwise |
+| `discount` | float | 0.0 at episode end, 1.0 otherwise |
+| `is_first` | bool | True for first step |
+| `is_last` | bool | True for last step |
+| `is_terminal` | bool | False (demonstrations are successful) |
+| `language_instruction` | string | Task description |
+
+## Loading the Dataset
+
+```python
+import tensorflow as tf
+
+# Load TFRecords
+dataset = tf.data.TFRecordDataset([
+    'fastumi_test/1.0.0/fastumi_test-train.tfrecord-00000-of-00001'
+])
+
+# Parse function
+def parse_episode(serialized):
+    features = {
+        'episode_id': tf.io.FixedLenFeature([], tf.string),
+        'num_steps': tf.io.FixedLenFeature([], tf.int64),
+        'steps/observation/image': tf.io.VarLenFeature(tf.string),
+        'steps/observation/state': tf.io.VarLenFeature(tf.float32),
+        'steps/action': tf.io.VarLenFeature(tf.float32),
+        'steps/reward': tf.io.VarLenFeature(tf.float32),
+        'steps/is_first': tf.io.VarLenFeature(tf.int64),
+        'steps/is_last': tf.io.VarLenFeature(tf.int64),
+        'steps/language_instruction': tf.io.VarLenFeature(tf.string),
+    }
+    return tf.io.parse_single_example(serialized, features)
+
+dataset = dataset.map(parse_episode)
+```
+
+## Data Source Mapping
+
+### Source Files (per session)
+```
+session_YYYYMMDD_HHMMSS/
+├── SLAM_Poses/
+│   └── slam_raw_baseframe.txt    -> observation/state, action
+├── RGB_Images/
+│   ├── video.mp4                 -> observation/image
+│   └── timestamps.csv            -> temporal alignment
+└── Clamp_Data/
+    └── clamp_data_tum.txt        (gripper data included in SLAM file)
+```
+
+## Citation
+
+If you use this dataset, please cite:
+
+```bibtex
+@misc{fastumi_rlds,
+    title={FastUMI Pro RLDS Dataset},
+    year={2024},
+}
+```

fastumi_test/1.0.0/dataset_info.json

@@ -0,0 +1,18 @@
+{
+  "name": "fastumi_test",
+  "version": "1.0.0",
+  "description": "FastUMI Pro robot manipulation dataset. Task: pick up the object",
+  "robot_type": "fastumi_pro",
+  "fps": 30,
+  "total_episodes": 12,
+  "total_steps": 6692,
+  "splits": {
+    "train": {
+      "num_examples": 12,
+      "num_bytes": -1
+    }
+  },
+  "citation": "",
+  "homepage": "",
+  "license": "MIT"
+}

fastumi_test/1.0.0/fastumi_test-train.tfrecord-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e53deacb585ec2d0ec7fbc55709de0ce00fac68d874026785d0d1762dd163fd9
+size 1933312950

fastumi_test/1.0.0/features.json

@@ -0,0 +1,61 @@
+{
+  "episode_id": {
+    "dtype": "string",
+    "shape": []
+  },
+  "num_steps": {
+    "dtype": "int64",
+    "shape": []
+  },
+  "steps": {
+    "observation": {
+      "image": {
+        "dtype": "uint8",
+        "shape": [
+          1080,
+          1920,
+          3
+        ],
+        "encoding": "JPEG"
+      },
+      "state": {
+        "dtype": "float32",
+        "shape": [
+          7
+        ],
+        "description": "End-effector pose: [x, y, z, roll, pitch, yaw, gripper]"
+      }
+    },
+    "action": {
+      "dtype": "float32",
+      "shape": [
+        7
+      ],
+      "description": "Target pose: [x, y, z, roll, pitch, yaw, gripper]"
+    },
+    "reward": {
+      "dtype": "float32",
+      "shape": []
+    },
+    "discount": {
+      "dtype": "float32",
+      "shape": []
+    },
+    "is_first": {
+      "dtype": "bool",
+      "shape": []
+    },
+    "is_last": {
+      "dtype": "bool",
+      "shape": []
+    },
+    "is_terminal": {
+      "dtype": "bool",
+      "shape": []
+    },
+    "language_instruction": {
+      "dtype": "string",
+      "shape": []
+    }
+  }
+}