OpenArm: real task instructions instead of 'default'; one name per camera in the README

README.md

@@ -68,7 +68,7 @@ Each G1 preview tiles the nine camera streams (color and stereo IR) above the li
 </tr>
 </table>
 
-Each OpenArm preview is about 8 s: three moments of the episode joined with short crossfades. The top half shows a scene camera beside the D435's lossless depth (warm is near, cool is far, black is no reading); below are every robot camera in color and the left image of its infrared stereo pair. The scene camera (a ZED) is only there for the previews and is not part of the dataset.
+Each OpenArm preview is about 8 s: three moments of the episode joined with short crossfades. The top half shows a scene camera beside the D435F's lossless depth (warm is near, cool is far, black is no reading); below are every robot camera in color and the left image of its infrared stereo pair. The scene camera (a ZED) is only there for the previews and is not part of the dataset.
 
 ## At a glance
 
@@ -78,7 +78,7 @@ Each OpenArm preview is about 8 s: three moments of the episode joined with shor
 | **Teleoperation** | rhoyn VR teleop, PICO 4 Ultra headset and controllers | rhoyn VR teleop, PICO 4 Ultra headset and controllers |
 | **Episodes** | 3, one per task · 12,633 frames · 7 min | 6, one per task · 20,841 frames · 11.6 min |
 | **Rate** | 30 Hz rows; depth at 15 fps; lidar, IMUs and VR packets at native rates | 30 Hz rows; depth at 15 fps; VR packets at native rate |
-| **Cameras** | 9 streams: head RealSense D435 + two wrist D405s, each color + stereo IR | 9 streams: RealSense D435F on top of the column + two wrist D405s, each color + stereo IR |
+| **Cameras** | 9 streams: RealSense D435i on the head + two wrist D405s, each color + stereo IR | 9 streams: RealSense D435F on top of the column + two wrist D405s, each color + stereo IR |
 | **Depth** | Lossless 16-bit from all three cameras | Lossless 16-bit from all three cameras |
 | **Lidar** | Livox MID-360 points, raw packets and IMU | None |
 | **IMUs** | Pelvis, chest, D435i | None |
@@ -98,22 +98,22 @@ Each OpenArm preview is about 8 s: three moments of the episode joined with shor
 
 Three more G1 tasks (connect four, jenga and a puzzle) are in the full six-task G1 download at [rhoyn.com/data-sample](https://rhoyn.com/data-sample).
 
-**Per G1 episode:** 9 synchronized camera streams (head + two wrist RealSense cameras, color and stereo IR), lossless 16-bit depth from all three cameras, a Livox MID-360 lidar point cloud, three IMUs, the full joint state and torque pipeline, and the raw VR teleoperation packets.
+**Per G1 episode:** 9 synchronized camera streams (a RealSense D435i on the head + two wrist cameras, color and stereo IR), lossless 16-bit depth from all three cameras, a Livox MID-360 lidar point cloud, three IMUs, the full joint state and torque pipeline, and the raw VR teleoperation packets.
 
 ### OpenArm
 
 | Folder | Task | Duration | Frames | Preview |
 |---|---|---|---|---|
-| `openarm/eggs/` | Cook an egg in a pan and scramble it | 192 s | 5,755 | [`previews/openarm/eggs.mp4`](previews/openarm/eggs.mp4) |
-| `openarm/connect_four/` | Pick up discs, pass them between the grippers and drop them into the board | 80 s | 2,388 | [`previews/openarm/connect_four.mp4`](previews/openarm/connect_four.mp4) |
-| `openarm/jenga/` | Push a block out of the Jenga tower, pass it between the grippers and place it on top | 94 s | 2,825 | [`previews/openarm/jenga.mp4`](previews/openarm/jenga.mp4) |
-| `openarm/puzzle/` | Pick up jigsaw puzzle pieces and place them | 104 s | 3,127 | [`previews/openarm/puzzle.mp4`](previews/openarm/puzzle.mp4) |
-| `openarm/socks/` | Pick up the socks and pair them | 108 s | 3,228 | [`previews/openarm/socks.mp4`](previews/openarm/socks.mp4) |
+| `openarm/eggs/` | Put butter in the pan, crack the egg into it and scramble it | 192 s | 5,755 | [`previews/openarm/eggs.mp4`](previews/openarm/eggs.mp4) |
+| `openarm/connect_four/` | Pick up a disc, pass it to the other gripper and drop it into the connect four board | 80 s | 2,388 | [`previews/openarm/connect_four.mp4`](previews/openarm/connect_four.mp4) |
+| `openarm/jenga/` | Push a block out of the Jenga tower, pass it to the other gripper and place it on top | 94 s | 2,825 | [`previews/openarm/jenga.mp4`](previews/openarm/jenga.mp4) |
+| `openarm/puzzle/` | Pick up the puzzle pieces and place them | 104 s | 3,127 | [`previews/openarm/puzzle.mp4`](previews/openarm/puzzle.mp4) |
+| `openarm/socks/` | Pick up the two socks and pair them together | 108 s | 3,228 | [`previews/openarm/socks.mp4`](previews/openarm/socks.mp4) |
 | `openarm/wipes/` | Open the pack, pull out a wet wipe and wipe the table | 117 s | 3,518 | [`previews/openarm/wipes.mp4`](previews/openarm/wipes.mp4) |
 
 **Per OpenArm episode:** 9 synchronized camera streams (a RealSense D435F on top of the column + two wrist cameras, color and stereo IR), lossless 16-bit depth from all three cameras, the full joint state and torque pipeline (PD gains, feedforward and velocity targets, requested/capped/commanded torque, torque budget and limiter state, measured joint torque, motor temperature), and the raw PICO VR teleoperation packets. There is no lidar and there are no IMUs on the OpenArm.
 
-Each folder holds one episode. The task string in `meta/tasks.parquet` is `default` for the OpenArm episodes; use the folder name as the task label.
+Each folder holds one episode. The task column below is the episode's language instruction, exactly as stored in `meta/tasks.parquet`.
 
 ## Loading
 
@@ -177,7 +177,7 @@ For policy learning on the G1, the operator-driven action is the arm and gripper
 ├── videos/          9 AV1 video streams, one per camera / IR imager
 ├── depth/           lossless 16-bit depth (FFV1 .mov) for 3 cameras, at 15 fps
 ├── lidar/           Livox MID-360 points, raw packets, and lidar IMU        (G1 only)
-├── d435_imu/        head camera IMU                                         (G1 only)
+├── d435_imu/        D435i IMU                                               (G1 only)
 └── pico/            raw Pico VR teleop controller packets
 ```
 
@@ -192,8 +192,8 @@ OpenArm episodes have no `lidar/` or `d435_imu/` folder and no `meta/lidar.json`
 
 | Key | Resolution | Description |
 |---|---|---|
-| `observation.images.d435` | 848×480 | RealSense D435 on the G1's head; D435F on top of the column on the OpenArm, tilted 20°; color |
-| `observation.images.d435_ir1`, `_ir2` | 848×480 | Head D435, stereo IR |
+| `observation.images.d435` | 848×480 | RealSense D435i on the head (G1) or D435F on top of the column, tilted 20° (OpenArm); color |
+| `observation.images.d435_ir1`, `_ir2` | 848×480 | D435i (G1) or D435F (OpenArm), stereo IR |
 | `observation.images.d405_left`, `d405_right` | 424×240 | Wrist RealSense D405, color |
 | `observation.images.d405_{left,right}_ir1`, `_ir2` | 424×240 | Wrist D405, stereo IR |
 
@@ -259,7 +259,7 @@ All six OpenArm episodes share an identical schema (`robot_type: openarm_v2_2x7d
 <details>
 <summary><b>Synchronization</b></summary>
 
-All streams are timestamped against one host clock. The data rows are a fixed 30 Hz grid (`observation.row_time_us`), and each camera contributes the frame nearest to each row. The cameras run freely and are not hardware-triggered, so each camera frame is within about ±16.7 ms (half a frame) of its row time, and head and wrist frames in the same row can be up to about 30 ms apart. The exact capture time and offset of every stream's frame are recorded per row in `observation.sensor_ts_us` and `observation.frame_offset_us`.
+All streams are timestamped against one host clock. The data rows are a fixed 30 Hz grid (`observation.row_time_us`), and each camera contributes the frame nearest to each row. The cameras run freely and are not hardware-triggered, so each camera frame is within about ±16.7 ms (half a frame) of its row time, and D435i/D435F and wrist frames in the same row can be up to about 30 ms apart. The exact capture time and offset of every stream's frame are recorded per row in `observation.sensor_ts_us` and `observation.frame_offset_us`.
 
 Lidar points, IMU samples (G1) and Pico packets (both robots) keep their native rates and carry their own timestamps. `meta/lidar.json`, `meta/d435_imu.json` and `meta/pico.json` describe the clocks.
 
@@ -268,7 +268,7 @@ Lidar points, IMU samples (G1) and Pico packets (both robots) keep their native
 <details>
 <summary><b>Depth</b></summary>
 
-Depth is lossless 16-bit (FFV1, `gray16le`), 424×240 at 15 fps, for all three cameras, on both robots. Depth frame *i* belongs to data row 2*i*. Convert to meters with `meters = raw * depth_scale` (0.001 for the D435, 0.0001 for the D405s, given per stream in `meta/depth.json`).
+Depth is lossless 16-bit (FFV1, `gray16le`), 424×240 at 15 fps, for all three cameras, on both robots. Depth frame *i* belongs to data row 2*i*. Convert to meters with `meters = raw * depth_scale` (0.001 for the D435i and D435F, 0.0001 for the D405s, given per stream in `meta/depth.json`).
 
 Two raw values mean "no valid depth" and should be masked:
 
@@ -284,7 +284,7 @@ The stats in `meta/depth.json` exclude both values.
 
 Camera intrinsics (all color, IR and depth streams) are RealSense factory calibration, read from each device at recording time.
 
-Camera-to-robot (and, on the G1, lidar-to-robot) extrinsics come from the robot model, not from a hand-eye calibration. On the G1, the head camera and lidar poses come from the Unitree G1 URDF; the wrist cameras' poses come from the CAD model of their 3D-printed mounts. On the OpenArm, the head camera pose comes from the CAD model of its 20° column mount and the wrist cameras' poses from their Dex1-1 brackets. Every transform carries a `source` field saying this. These poses are suitable for coarse fusion. If you need precise depth-to-robot or lidar-to-camera alignment, plan to refine them.
+Camera-to-robot (and, on the G1, lidar-to-robot) extrinsics come from the robot model, not from a hand-eye calibration. On the G1, the D435i and lidar poses come from the Unitree G1 URDF; the wrist cameras' poses come from the CAD model of their 3D-printed mounts. On the OpenArm, the D435F pose comes from the CAD model of its 20° column mount and the wrist cameras' poses from their Dex1-1 brackets. Every transform carries a `source` field saying this. These poses are suitable for coarse fusion. If you need precise depth-to-robot or lidar-to-camera alignment, plan to refine them.
 
 </details>
 

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