LEAP Hand assets & trained retargeter weights + retargeting instability with public URDF

Could you please share the **LEAP Hand assets** (URDF/meshes/configs) and, if possible, the **trained retargeter weights** used in your experiments?

I trained and evaluated exactly as described in the README, using the provided training data. Losses converge, but the LEAP hand does not track** the video and instead produces unstable/unnatural joint behavior.

LEAP URDF used:

Steps followed as in README, loss curves converge, but:
- The hand **does not follow** the driving motion.
- Fingers **jitter or hyperextend**, especially during multi-finger gestures.
- Wrist/base shows **unnatural compensations**.
- Overall motion looks **miscalibrated** compared to the video.

## What I’ve checked
- Training ran without errors; losses decreased smoothly.
- URDF loaded fine; joint limits parsed correctly.
- Units/inertias look consistent.
- Tried clamping predictions to joint limits and adding smoothing → instability persists.

My hypothesis is that the public LEAP URDF may **not match** the one used in your experiments (frames, joint limits, actuator modeling, inertias) or the model may require **normalization constants, calibration settings, or different hyperparameters** not mentioned in the README.

## Requests
1. **LEAP assets**: the canonical URDF/meshes and joint/limit definitions used in your work.
2. **Pretrained weights**: LEAP retargeter checkpoint + config (loss weights, architecture, inference filters).

Thanks in advance for considering a release of the LEAP assets and weights! It would make reproducing results and resolving this mismatch much easier.