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Planar Reaching with Difference Target Propagation

This repository implements a learning framework for planar reaching movements using Difference Target Propagation (DTP). The implementation includes both forward kinematics for a 2-joint planar arm and a DTP network for learning reaching movements.

Overview

The project consists of two main components:

  1. Planar Arm Kinematics: A 2-joint robotic arm model with:

    • Forward kinematics for end-effector positioning
    • Inverse kinematics for joint angle calculation
    • Workspace constraints and joint limits
  2. Difference Target Propagation Network: A neural network that learns to generate reaching movements with:

    • Direct DTP
    • Feedback weight learning through local target computation
    • Feedforward weight through DRL

Project Structure

esyniawa-dtp-planar_reaching/
├── LICENSE               # MIT License
├── environment.py        # Environment setup and data handling
├── main.py               # Training script and network creation (example)
├── kinematics/          
│   ├── planar_arms.py    # Planar arm implementation
│   └── utils.py          # Kinematics utilities
└── network/
    ├── dtp_networks.py           # DDTP networks implementation
    └── layers.py                 # Feedforward and feedback layer definitions

Implementation Details

Planar Arm

  • Two-joint arm with shoulder and elbow
  • Configurable link lengths and joint limits
  • Support for both left and right arm configurations

DTP Network

  • Customizable layer architecture
  • Forward and feedback weight optimization
  • Local target computation for each layer
  • MSE-based reconstruction loss
  • Built-in support for batch processing

Dependencies

  • PyTorch
  • Matplotlib
  • tqdm
  • pandas

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

Based on research in target propagation and biological learning mechanisms, particularly:

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