PDF-HR for PHUMA H1_2 (27 DoF)
Hierarchical Pose Distance Field (PDF-HR) model trained for the PHUMA H1_2 humanoid robot (27 active DoF).
This model learns a continuous distance field to the feasible pose manifold using a hierarchical joint encoder architecture inspired by Appendix B of the PDF-HR paper.
Model Overview
- Robot: PHUMA H1_2
- Active joints: 27 (revolute)
- Feature dimension per joint: 16
- Hierarchical encoder per joint
- Final MLP: 3 layers × 256 neurons
- Activation: ELU
- Output activation: Softplus (guarantees distance ≥ 0)
- Loss: L1 (MAE)
- Scheduler: CosineAnnealingLR
- Epochs: 50
- Batch size: 4096
- Optimizer: Adam (lr = 1e-3)
Architecture
Each joint has an independent local encoder:
- Root joints receive only their joint angle.
- Child joints receive their angle + parent feature.
- All joint features are concatenated.
- A final MLP predicts a scalar distance to the pose manifold.
Dataset
This model was trained using:
PHUMA H1_2 PDF-HR Dataset
Dataset Contents
positive_poses_all.npy → positive poses (distance = 0)near_poses.npy + near_distances.npyinterp_poses.npy + interp_distances.npy
All arrays are in NumPy .npy format.
Robot specifications:
- Model: H1_2 (PHUMA)
- Degrees of Freedom: 27 active joints
- Joint order: Matches URDF hierarchy and training script definition
Author
Josué Abad
Lab
NONHUMAN Robotics – Perú
