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---
tags:
- robotics
- lerobot
- stringman
task_categories:
- robotics
---
# simple_grasp_spin
[naavox/simple_grasp](https://huggingface.co/datasets/naavox/simple_grasp) with the
`spin` state field recovered. Same 370 episodes, same 148,916 frames, same videos - the
only difference is one extra number per frame at the end of `observation.state`.
`spin` is the gripper camera's heading in the room. The original recording never logged
it, which makes the dataset unusable for visual servoing mining: labelling an approach
means projecting a room point into the camera, and a camera of unknown heading projects
nothing.
## How it was recovered
Not estimated - reconstructed, from two things the recording did keep:
1. Every named target carries a `bearing`, written as `room_angle - spin`. No target was
ever detected in this recording, so they all sit at the room origin, which shows up in
the data as `distance` being exactly the gripper's horizontal distance from it. The
room angle is then known from the gripper position alone, so
`spin = atan2(-x, -y) - bearing` is an identity. It degenerates only near the origin,
where the direction back to it stops being well defined.
2. `spin` moves with `wrist_angle` and nothing else, so the difference between them is a
calibration constant. Measuring it on the frames where the bearing is well conditioned
fills in the rest exactly.
The constant came out flat to within 0.064 degrees inside every episode, and took three
distinct values across the dataset - three calibration sessions.
## Coverage
`spin` is filled for **96.2% of frames, 355 of 370 episodes**. The other 15 never left
the neighbourhood of the room origin, so their heading could not be measured; their rows
carry 0.0 and should be skipped rather than trusted.
## Reproducing
python -m nf_robot.ml.visual_servoing.recover_spin \
--repo_id naavox/simple_grasp --into simple_grasp_spin --videos
from [cranebot3-firmware](https://github.com/naavox/cranebot3-firmware),
`nf_robot/ml/visual_servoing/recover_spin.py`.