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