YOCO: You Only Calibrate Once! Fast Mocap Calibration for Dexterous Teleoperation
We present YOCO, a fast few-shot, fine-tuning-free calibration framework that corrects biased hand-pose streams from a small set of paired raw and target poses.
Key points
- Dexterous teleoperation requires reliable human-hand state estimations.
- Instead of optimizing a separate model for every operator or session, YOCO conditions a calibration HyperNet on the paired examples and predicts LoRA-style updates for a frozen MANO hand-estimation module, turning per-user calibration into a lightweight feed-forward adaptation step while preserving the geometric prior of MANO and the efficiency of a compact estimator.
- We train YOCO with synthetic drift augmentations on InterHand2.6M and evaluate on augmented InterHand sequences, offline real glove data, and dexterous teleoperation tasks.
- Across these settings, YOCO improves calibration efficiency, hand-state estimation quality and teleoperation performance compared with uncalibrated input and standard calibration baselines.
Sources (1)
- [1]YOCO: You Only Calibrate Once! Fast Mocap Calibration for Dexterous TeleoperationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 10:34 AM
We present YOCO, a fast few-shot, fine-tuning-free calibration framework that corrects biased hand-pose streams from a small set of paired raw and target poses.
Dexterous teleoperation requires reliable human-hand state estimations.
Extractive summary: sentences quoted from the sources.