Update README.md

README.md

@@ -10,152 +10,54 @@ configs:
 ---
 
 This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).
+# Dataset Card for **Smol-LIBERO**
 
-## Dataset Description
+## Dataset Summary
+Smol-LIBERO is a **compact version of the LIBERO benchmark**, built to make experimentation fast and accessible.  
+At just **1.79 GB** (compared to ~34 GB for the full LIBERO), it contains fewer trajectories and cameras while keeping the same multimodal structure.  
 
+Each sample includes:
+- **Images** from two fixed cameras  
+- **Two types of robot state** (end-effector pose + gripper, and full 7-DoF joint positions)  
+- **Actions** (7-DoF joint commands)  
 
+This setup is especially useful for comparing **low-dimensional state inputs** with **high-dimensional visual inputs**, or combining them in multimodal training.
 
-- **Homepage:** [More Information Needed]
-- **Paper:** [More Information Needed]
-- **License:** apache-2.0
+---
 
 ## Dataset Structure
 
-[meta/info.json](meta/info.json):
-```json
-{
-    "codebase_version": "v2.1",
-    "robot_type": null,
-    "total_episodes": 50,
-    "total_frames": 13021,
-    "total_tasks": 1,
-    "total_videos": 0,
-    "total_chunks": 1,
-    "chunks_size": 1000,
-    "fps": 20,
-    "splits": {
-        "train": "0:50"
-    },
-    "data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
-    "video_path": null,
-    "features": {
-        "observation.images.image": {
-            "dtype": "image",
-            "shape": [
-                256,
-                256,
-                3
-            ],
-            "names": [
-                "height",
-                "width",
-                "channel"
-            ]
-        },
-        "observation.images.wrist_image": {
-            "dtype": "image",
-            "shape": [
-                256,
-                256,
-                3
-            ],
-            "names": [
-                "height",
-                "width",
-                "channel"
-            ]
-        },
-        "observation.state.end_effector": {
-            "dtype": "float32",
-            "shape": [
-                8
-            ],
-            "names": {
-                "motors": [
-                    "x",
-                    "y",
-                    "z",
-                    "roll",
-                    "pitch",
-                    "yaw",
-                    "gripper",
-                    "gripper"
-                ]
-            }
-        },
-        "observation.state.joint": {
-            "dtype": "float32",
-            "shape": [
-                7
-            ],
-            "names": {
-                "motors": [
-                    "joint_1",
-                    "joint_2",
-                    "joint_3",
-                    "joint_4",
-                    "joint_5",
-                    "joint_6",
-                    "joint_7"
-                ]
-            }
-        },
-        "action": {
-            "dtype": "float32",
-            "shape": [
-                7
-            ],
-            "names": {
-                "motors": [
-                    "x",
-                    "y",
-                    "z",
-                    "roll",
-                    "pitch",
-                    "yaw",
-                    "gripper"
-                ]
-            }
-        },
-        "timestamp": {
-            "dtype": "float32",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "frame_index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "episode_index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        },
-        "task_index": {
-            "dtype": "int64",
-            "shape": [
-                1
-            ],
-            "names": null
-        }
-    }
-}
-```
+### Data Fields
+- **`observation.images.image`**: 256×256×3 RGB image (camera 1)  
+- **`observation.images.image2`**: 256×256×3 RGB image (camera 2)  
+- **`observation.state`** *(8 floats)*: end-effector Cartesian pose + gripper  
+  `[x, y, z, roll, pitch, yaw, gripper, gripper]`  
+- **`observation.state.joint`** *(7 floats)*: full joint angles  
+  `[joint_1, …, joint_7]`  
+- **`action`** *(7 floats)*: target joint commands  
+
+---
+
+## Why is it smaller than LIBERO?
+- **Fewer trajectories/tasks** → subset of the full benchmark  
+- **Only two camera views** → reduced visual redundancy  
+- **Reduced total frames** → shorter episodes or lower FPS  
+
+That’s why Smol-LIBERO is **1.79 GB instead of 34 GB**.
+
+---
+
+## Intended Uses
+- Quick prototyping and debugging  
+- Comparing joint-space vs. Cartesian state inputs  
+- Training small VLA baselines before scaling to LIBERO  
+
+---
 
+## Limitations
+- Smaller task and visual diversity compared to LIBERO  
+- Only two fixed camera views  
+- May not fully represent generalization behavior on larger benchmarks  
 
 ## Citation