Update README.md

README.md

@@ -26,9 +26,10 @@ The task evaluates in-hand dexterous manipulation with a simulated Shadow Hand.
 ## Dataset Description
 
 - **Task:** Manipulate Block / Block Rotate-Z
-- **Environment:** simulated Shadow Hand dexterous manipulation environment
+- **Environment:** `HandManipulateBlockRotateXYZ-v1`
 - **Embodiment:** 24-joint Shadow Hand, with the full environment containing up to 30 degrees of freedom
 - **Number of trajectories:** 5,000 expert trajectories
+- **Number of transitions:** 123,711
 - **Collection method:** Truncated Quantile Critics with Hindsight Experience Replay (TQC+HER)
 - **Modality:** state-action robot trajectories
 - **Primary file:** `manipulate_block_5000.h5`
@@ -61,7 +62,32 @@ The dataset is distributed as a single HDF5 file:
 manipulate_block_5000.h5
 ```
 
-The archive contains 5,000 expert trajectories. To inspect the exact groups, fields, shapes, and dtypes in the released file:
+The archive contains **5,000 expert trajectories** and **123,711 transition steps** collected in `HandManipulateBlockRotateXYZ-v1`.
+
+| Field | Shape | Dtype | Description |
+| --- | --- | --- | --- |
+| `observation` | `(123711, 1, 61)` | `float32` | Environment observations |
+| `action` | `(123711, 1, 20)` | `float32` | Shadow Hand actions |
+| `reward` | `(123711, 1)` | `float32` | Sparse task rewards |
+| `terminal` | `(123711, 1)` | `bool` | Stored terminal indicators |
+| `info/achieved_goal` | `(123711, 1, 7)` | `float32` | Achieved goal states |
+| `info/desired_goal` | `(123711, 1, 7)` | `float32` | Desired goal states |
+| `info/is_success` | `(123711,)` | `float32` | Task-success indicators |
+| `info/object_qvel` | `(123711, 6)` | `float64` | Object velocities |
+| `info/robot_qpos` | `(123711, 24)` | `float64` | Shadow Hand joint positions |
+| `info/robot_qvel` | `(123711, 24)` | `float64` | Shadow Hand joint velocities |
+| `info/timeout` | `(123711,)` | `bool` | Stored timeout indicators |
+
+The sparse reward is `-1` before task completion and `0` upon success. The file contains 5,000 positive `info/is_success` markers, corresponding to the 5,000 collected expert trajectories. The stored `terminal` and `info/timeout` arrays do not mark every trajectory boundary; use `info/is_success` to recover the successful trajectory boundaries in collection order.
+
+The HDF5 root attributes are:
+
+| Attribute | Value |
+| --- | --- |
+| `env_name` | `HandManipulateBlockRotateXYZ-v1` |
+| `total_episodes` | `5000` |
+
+To inspect the groups, fields, shapes, and dtypes programmatically:
 
 ```python
 import h5py