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Reinforcement learning approach to optimal robotics control

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NeuroRobotics: Reinforcement Learning Infrastructure for Physics-Based Simulation

Overview

NeuroRobotics is a learning project designed for physics-based simulation using reinforcement learning. It incorporates Bullet physics and stable baselines3 to facilitate realistic and effective robot control simulations.

Features

  • Physics-Based Simulation: Employs Bullet physics for accurate and dynamic simulations.
  • Reinforcement Learning: Built with stable baselines3, enabling a robust learning environment for robots.
  • URDF Robot Coding: Supports detailed robot coding using URDF (Unified Robot Description Format), allowing for precise manipulation and control.
  • Task Focused: Primarily designed for tasks involving object picking and relocation.

Application

This project is a learning tool aimed at developing and testing robot control algorithms in a simulated environment, particularly for object manipulation tasks.


Note: This project is for educational purposes and is a part of my ongoing learning in robotics and simulation.

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