model.py

import torch
import torch.nn as nn

class HumanoidControlModel(nn.Module):
def __init__(self, input_size=128, hidden_size=256, output_size=64):
super().__init__()
self.net = nn.Sequential(
nn.Linear(input_size, hidden_size),
nn.ReLU(),
nn.Linear(hidden_size, output_size)
)

def forward(self, x):
return self.net(x)

README

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+# Humanoid Robot Control Model
+
+This model is designed as a base neural network architecture for humanoid robot control and motion learning.
+
+## Purpose
+- Humanoid locomotion
+- Joint control prediction
+- Robotics simulation and reinforcement learning
+
+## Architecture
+- Feedforward Neural Network (MLP)
+- Suitable for imitation learning and RL fine-tuning
+
+## Training Usage
+This model can be fine-tuned using humanoid robotics datasets such as:
+- Motion capture data
+- Joint angle trajectories
+- Sensor-to-action mappings
+
+## Framework
+- PyTorch
+- Robotics / Humanoid AI
+
+## Status
+Base model prepared for further training and experimentation.