How to learn robo-gym systematically

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Whether you are a beginner or an experienced professional, when learning a new IT technology, you need to take a systematic approach to learning. So as a technical er, how do you systematically learn IT technology.

In recent years, the application of deep reinforcement learning (DRL) to complex tasks in robotics has proven to be very successful. However, most publications focus either on applying it in simulations or in the real world. While there are good examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional work and fine-tuning to make the setup work effectively. To increase the use of DRL on real robots and reduce the gap between simulated and real-world robots, we propose an open-source toolkit: robo-gym. We demonstrate a unified setup for simulated and real environments, enabling seamless transfer of training applied in simulation to robots. We demonstrate the capability and effectiveness of the framework with two real-world applications that characterize industrial robotics: mobile robots and robotic arms. The distributed capability of the framework brings many advantages, such as using distributed algorithms, separating the workload of simulation and training onto different physical machines, and future opportunities to train simultaneously in simulation and the real world. Finally, we provide an overview and comparison with other commonly used state-of-the-art DRL frameworks.

Trilogy:

• academic paper
• technical manual
• source code repository

1 academic paper

2 technical manual

 

3 source code repository

 

 

Robo-Gym is an open-source toolkit for distributed deep reinforcement learning on real and simulated robots. If you want to learn Robo-Gym, here are some suggestions:

1. Read the official documentation: Robo-Gym's official documentation provides detailed information about the toolkit, including installation instructions, API reference, and sample code. Make sure you read the official documentation in order to understand how to use Robo-Gym properly.
2. Know the basics of reinforcement learning: Robo-Gym is a reinforcement learning framework, so you need to know the basics of reinforcement learning. You can find some free reinforcement learning tutorials online, such as MIT's "Introduction to Reinforcement Learning" course.
3. Learn Deep Learning Basics: Robo-Gym uses deep neural networks as Q-function and policy function. Therefore, you need to understand the basic concepts and terminology of deep learning, such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning algorithms.
4. Reference example code: Robo-Gym provides a number of example codes that demonstrate how to use the toolkit for deep reinforcement learning. You can refer to these sample codes to learn how to implement various reinforcement learning algorithms using Robo-Gym.
5. Participate in community discussions: Robo-Gym has an active community where you can ask questions, answer questions and share experiences. By participating in community discussions, you can get help from other users and learn about the latest developments in the toolkit.
6. Practice: Finally, to truly master Robo-Gym, you need practice. Try using Robo-Gym to solve some reinforcement learning problems, such as maze problems and robot control problems. With practice, you'll develop a deep understanding of the toolkit's capabilities and limitations, and continually improve your skill level.

1. Introduction of Robo-Gym technology

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Robo-Gym is an open-source toolkit for distributed reinforcement learning on real and simulated robots. It provides a range of reinforcement learning environments including robotics tasks for simulation and real-world robotics. Additionally, it provides tools that help in creating new environments with different robots and sensors.

Key features of Robo-Gym include:

1. It can be seamlessly converted on real and simulated robots, realizing a seamless transition from simulated training to real robot applications.
2. The built-in distributed function can use distributed algorithms and distributed hardware.
3. Based on open source software, applications can be developed on own hardware without incurring cloud service charges or software license fees.
4. Two commercially available industrial robots are integrated: MiR 100 and UR 10 (coming soon).
5. Successfully deployed to train a DRL algorithm to solve two different tasks in simulation, enabling the task to be solved on a real robot without any further training in the real world.
6. Tested on Mac OS Mojave, Catalina and Ubuntu 16.04 LTS, may work on latest Mac and Linux operating systems. Requires Python 3.7.4 or later.

In conclusion, Robo-Gym is a powerful toolkit for robot learning and training, which can help robots quickly adapt to new environments and learn new skills.

2. Preparation before study

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Learning Robo-Gym requires the following preparations:

1. Install the necessary software: You need to install Python 3.7.4 or higher and use a virtual environment or conda environment to manage Robo-Gym and related dependencies. Robo-Gym has been tested on Mac OS Mojave, Catalina and Ubuntu 16.04 LTS, so you can choose the version suitable for your operating system to install.
2. Understand the basics of reinforcement learning: Robo-Gym is a reinforcement learning framework, so you need to understand the basics of reinforcement learning, such as concepts such as states, actions, rewards, and Q functions. You can find some free reinforcement learning tutorials online, such as MIT's "Introduction to Reinforcement Learning" course.
3. Understand the basics of deep learning: Robo-Gym uses deep neural networks as Q functions and policy functions, so you need to understand the basic concepts and terms of deep learning, such as convolutional neural networks, recurrent neural networks, and deep reinforcement learning algorithms.
4. Understand robotics basics: You need to understand some robotics fundamentals, such as robot kinematics and dynamics, in order to understand robot simulation and training in Robo-Gym.
5. Understand distributed computing and parallel computing: Robo-Gym supports distributed training and parallel computing, so you need to understand the basic concepts of distributed computing and parallel computing, such as MPI and multi-process.
6. Participate in community discussions: Robo-Gym has an active community where you can ask questions, answer questions and share experiences. By participating in community discussions, you can get help from other users and learn about the latest developments in the toolkit.

In conclusion, learning Robo-Gym requires some preparation and foundation, but once you master the toolkit, you will be able to use it for robotic reinforcement learning, helping robots quickly adapt to new environments and learn new skills.

Three, the specific operation of learning

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The specific operation of Robo-Gym learning includes the following steps:

1. Install the necessary software, including Python and Robo-Gym.
2. To understand the basics of reinforcement learning, you can find some free reinforcement learning tutorials on the Internet to learn.
3. Understanding the basics of deep learning requires knowledge of deep neural networks and deep reinforcement learning algorithms.
4. Understanding the basics of robotics requires knowledge of robot kinematics and dynamics.
5. To understand distributed computing and parallel computing, you need to understand MPI and multi-process knowledge.
6. Create a virtual environment or conda environment to manage Robo-Gym and related dependencies.
7. Install Robo-Gym and other necessary dependencies.
8. Import necessary libraries and environments, such as mujoco_py, jsonnet and robogym, etc.
9. Run the official example or other sample code, such as running the "examine.py" script for environment analysis and parameter passing error checking.
10. Conduct robot reinforcement learning training, use the reinforcement learning environment and tools provided by Robo-Gym for robot control and training.
11. Participate in community discussions, Robo-Gym has an active community where you can ask questions, answer questions and share experiences.

In short, the specific operation of Robo-Gym learning requires certain preparation and foundation, but once you master the toolkit, you will be able to use it for robot reinforcement learning, helping robots quickly adapt to new environments and learn new skills.

4. How to consolidate learning

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Robo-Gym can reinforce learning in several ways:

1. Real-time data feedback: Robo-Gym can display training data in real time and save it to the cloud. By collecting and analyzing data, the accuracy of screening can be greatly improved, and the actual force and movement of each part of the exercise can be viewed more accurately. This allows you to individually adapt your training to your requirements.
2. Interchangeability of simulated and real robots: Robo-Gym provides interchangeability of simulated and real robots, which can be trained in a simulated environment and then directly applied on real robots. This reduces the risk of mishandling in real-world environments, while also saving costs.
3. Distributed function: Robo-Gym supports distributed algorithms and distributed hardware, and can use multiple computers or multiple processors for training to speed up learning.
4. Multiple training methods: Robo-Gym can support a variety of different training methods, such as leg presses, knee extensions, and bent-over rows. These different training methods can help the robot better understand the task and consolidate the learning results.
5. Integrates a variety of robots: Robo-Gym integrates commercial industrial robots such as MiR 100 and UR 10, and plans to launch more robots in the future. These robots can help Robo-Gym better understand robot behavior and tasks in different scenarios and consolidate learning results.

To sum up, Robo-Gym consolidates learning results and improves the intelligence level of robots through real-time data feedback, interchangeability of simulated and real robots, distributed functions, multiple training methods, and integration of multiple robots.

 

 

Reminder: The above content is for reference only and can be distributed by itself. Please delete unnecessary content before publishing your work.