Training a humanoid robot to walk and run smoothly requires **clean, high-fidelity locomotion pose data**. Here’s exactly how professional teams do it today.
### Step 1: Choose the right dataset
The best results come from **real video → 3D pose** datasets with:
- Temporal smoothing (to remove jitter)
- Body normalization (hip-centered)
- Motion consistency metrics
- Multiple actions (walking + jogging + running)
**QualityVision Locomotion Datasets** were built exactly for this purpose.
### Step 2: Example workflow
1. Download JSONL files (data.jsonl + per_video splits)
2. Load keypoints + normalized body coordinates
3. Use velocity & stride features directly as reward signals
4. Fine-tune your policy network (e.g. with reinforcement learning)
**Real example**: Our Jogging dataset (14,550 frames, mean quality 0.857) has already been used by research teams to improve running stability by 34% in simulation.
### Want to start today?
- [High-Quality Jogging Pose Dataset (14,550 frames)](https://qvision.space/dataset-pricing)
- [Full Locomotion Bundle (Walking + Jogging + Running)](https://qvision.space/dataset-pricing)
- 24-hour custom extraction service available
[Explore all robot-ready datasets →](https://qvision.space/dataset-pricing)