ATEM v1.2.7

What’s New

New Features

1. Interpreter Class:
   • Introduced a new Interpreter class for direct TFLite model inference.
   • Added functionality to interpret task sequences and sensor data.
   • Supports saving interpretation results to a JSON file for easy logging and debugging.
2. Improved API Usability:
   • Enhanced the AdaptiveModel and ModelTrainer classes with better task prediction and sequence encoding.
   • Updated task sequence padding logic for increased flexibility.
3. Increased Example Input Coverage:
   • Added more robust examples for inputs to ensure clarity in documentation and usage.

ATEM v1.2.6

Whats New?

• Added improved documentation for getting started with ATEM

ATEM v1.2.5

ATEM v1.2.5

Whats new?

Improved ModelTrainer:

• Added the ability to dynamically adjust the maximum task sequence length with a new set_max_length method.
• Enhanced error handling for better user feedback when tasks or sequences are invalid.

Simplified Documentation:

• Unified docs.md with README.md for streamlined package documentation and setup.

New Features:

• Added terminal output color coding using colorama for a more engaging user experience during training.

Bug Fixes:

• Resolved issues with task sequence encoding and padding that caused errors with negative index padding.
• Fixed incorrect mappings in the task encoder.

This version is fully backward-compatible with v1.2.4. Users are encouraged to upgrade for improved stability and features.

ATEM v1.2.1

ATEM v1.2.1

1. Adaptive Model Integration

• Introduced the AdaptiveModel class for real-time task prediction based on sensor data.
• Enhanced predict_next_task function for dynamic task scheduling using TensorFlow Lite models.

2. Improved Package Structure

• Refactored the project into a cleaner directory structure:
• Core functionalities under atem_core/core.
• Utility functions under atem_core/utils.
• Added proper __init__.py files to support package discovery and modular use.

3. Real-World Simulation

• Developed a real-time simulation workflow using mock sensor data to test adaptive task prediction.

ATEM v1.0.0

ATEM Adaptive Robot Control Framework v1.0.0

The ATEM (Adaptive Task Execution Manager) is a Python-based machine learning framework designed for FTC robots to dynamically adapt their task execution strategies. It integrates a TensorFlow Lite model with A* pathfinding to optimize autonomous operations on the FTC field.

Features

AI-Powered Task Selection:

• Processes real-time sensor data (e.g., time, distance, gyro, battery).
• Predicts the next optimal task using a trained TensorFlow Lite model.

A Pathfinding for Navigation*:

• Robot moves autonomously on the FTC field, avoiding obstacles.
• Compatible with FTC wheel and arm control functions.

Modular Design:

• Python package structure allows easy integration into robotics projects.
• Java interoperability to run directly on FTC robot controllers.

Robust Training Pipeline:

• Train adaptive models to fine-tune task prioritization.
• Generate, interpret, and deploy tasks dynamically.