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tags:
- robotics
- humanoid
- reinforcement-learning
- double-dqn
- pytorch
---
# HumanoidTTT — Consolidation Policy
Initial Double-DQN consolidation weights for
[HumaniodTTT: Test-Time Capability Reuse for Efficient Humanoid Control](https://github.com/AIGeeksGroup/HumaniodTTT).
## Model
The policy scores retention actions for a finite-capacity capability store. Each
action has a 71-dimensional consolidation feature and is scored by a shared
`71 → 128 → 64 → 1` ReLU network with **17,537 parameters**. When the ten-slot store
is full, the actions are `SKIP` or `REPLACE(j)`. Online Double-DQN updates use the
fraction of subsequent requests with successful reuse between consecutive
full-store qualified-miss decisions.
## Release files
| File | Contents |
| --- | --- |
| `consolidation_policy.pt` | Initial FP32 PyTorch scorer state dictionary, seed 83001. |
| `config.json` | Architecture, feature layout, action mapping, online settings, and weight checksum. |
| `SHA256SUMS` | File checksums. |
This checkpoint is the starting point before online adaptation. It does not
contain a prefilled capability store or optimizer/replay state.
## Usage
Install the code and download dependencies:
```bash
git clone https://github.com/AIGeeksGroup/HumaniodTTT.git
cd HumaniodTTT
pip install -e .
pip install huggingface_hub
```
```python
import torch
from huggingface_hub import hf_hub_download
from humanoid_ttt import ConsolidationPolicy
path = hf_hub_download("AIGeeksGroup/HumanoidTTT", "consolidation_policy.pt")
weights = torch.load(path, map_location="cpu", weights_only=True)
policy = ConsolidationPolicy(weights, capacity=10, online_learning_rate=3e-5, gamma=0.95)
```
See the [GitHub README](https://github.com/AIGeeksGroup/HumaniodTTT#using-the-components)
for entry applicability, execution feedback, and update interfaces. Pin the model
revision and code commit when reproducing experiments.
## Dependencies and scope
The 45-dimensional A2 entry-applicability module uses state features and geometric
certificates; it has no separate neural-network checkpoint. Applications supply
motion-specific certificates, qualification evidence, and the execution runtime.
Download frozen generation and tracking models from
[OMG](https://github.com/Tsinghua-MARS-Lab/OMG) and
[HoloMotion](https://github.com/HorizonRobotics/HoloMotion).
The released scorer weights alone do not constitute an end-to-end robot controller.
Successful loading verifies network compatibility, not complete experimental reproduction.
|