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How can you choose the right ML framework for Robotics?

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Machine learning (ML) is a powerful tool for robotics, enabling tasks such as perception, control, planning, and learning from data. However, choosing the right ML framework for your robotics project can be challenging, as there are many options available with different features, benefits, and trade-offs. In this article, we will guide you through some key criteria and questions to consider when selecting an ML framework for robotics, and introduce some popular and useful frameworks that you can explore.

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RAUNAK CHOUDHARY?? ????

Robopreneur Empowering the next generation to lead India into a tech-driven future ?? Recognized for Excellence in…
Rushikesh Deshmukh

Robotics Engineer | AI Engineer at Lumasort LLC
Abhinav Upadhyay

Associate Director AI | Generative AI, Deep Learning, 2D/3D Computer vision, NLP, Robotics

1 Criteria for ML frameworks

Before diving into the details of different ML frameworks, it's important to first define your goals, needs, and constraints for your robotics project. When making this decision, you should consider the type and complexity of the ML problem you need to solve, such as supervised, unsupervised, or reinforcement learning. Additionally, you should look at hardware and software requirements and compatibility, such as the sensors, processors, memory, and operating systems that are needed for your robot. Usability and flexibility of the ML framework are also important factors to consider. This includes the user-friendliness of the framework's interface and documentation, as well as how much control you have over the model's architecture. Finally, performance and scalability of the ML framework should be taken into account. This means looking at how fast and accurate the training and inference process is, as well as how easy it is to scale up or down the model's size.

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Rushikesh Deshmukh

Robotics Engineer | AI Engineer at Lumasort LLC
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Choosing the right framework come down to defining the problem statement and having a clear understanding of what you want to achieve vs what kind of data you have. If you want to do classification and know the classes you want to sort to then go with Supervised Learning. Don’t know the exact grouping but want to find patterns and groups in the data, go with unsupervised. If the data is of images then neural network can process it better with CNN layers. If the data is of temporal nature then RNN or LSTMs can be the way you go. Mostly, just trying out different projects and approaches will give you the knowledge and experience to make correct choices.

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Abhinav Upadhyay

Associate Director AI | Generative AI, Deep Learning, 2D/3D Computer vision, NLP, Robotics
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Existing ML/DL frameworks like PyTorch, TensorFlow, and Keras are commonly used to integrate intelligence into robots. Computer vision models such as CNNs and Transformer-based models enhance perception and grasping tasks. Large language models aid in plan generation and interaction. Open-source models like RT-X from Google and VIMA from Nvidia are available for robotics training. Additionally, robotics frameworks like Nvidia Isaac provide ML capabilities, streamlining development. These frameworks empower developers to leverage cutting-edge AI techniques for various robotic applications, fostering innovation and advancement in the field.

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RAUNAK CHOUDHARY?? ????

Robopreneur Empowering the next generation to lead India into a tech-driven future ?? Recognized for Excellence in Robotics with State and National Awards
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Before choosing a ML framework for your robotics project, define your goals, needs, and constraints. Consider the type and complexity of the ML problem, hardware and software requirements, usability and flexibility, and performance and scalability. These factors can help you select the most suitable and efficient ML framework for your robotics project, and ensure that it meets your expectations and requirements. By choosing the right ML framework, you can improve the accuracy, reliability, and adaptability of your robotics system, and enhance its performance, usability, and impact in various applications.

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2 Popular ML frameworks

Based on the criteria above, you can narrow down your choices of ML frameworks and compare their pros and cons. Some of the most popular and widely used ML frameworks for robotics that you can explore are TensorFlow, PyTorch, and ROS (Robot Operating System). TensorFlow is a comprehensive and versatile framework that supports a wide range of ML tasks, from basic operations to advanced deep learning models. It has a high-level API called Keras that simplifies the model creation and training process, as well as tools for distributed computing, visualization, debugging, and deployment. PyTorch is a flexible and dynamic framework that enables fast and easy prototyping and experimentation of ML models. It has a Python-based interface that integrates well with other Python libraries, such as NumPy and SciPy. ROS (Robot Operating System) is not an ML framework, but rather a middleware that provides a standard and modular way of developing and running robotics software. It has a collection of libraries, tools, and packages that facilitate communication, coordination, and integration of different components of a robot's system. All these frameworks are compatible with various hardware platforms such as CPUs, GPUs, TPUs, embedded devices; they can run on multiple operating systems such as Linux, Windows, MacOS, Android, and iOS.

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RAUNAK CHOUDHARY?? ????

Robopreneur Empowering the next generation to lead India into a tech-driven future ?? Recognized for Excellence in Robotics with State and National Awards
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Based on your criteria, consider TensorFlow, PyTorch, or ROS for your robotics project. TensorFlow is a comprehensive and versatile framework for various ML tasks, with a high-level API and tools for distributed computing. PyTorch is a flexible and dynamic framework for fast and easy prototyping and experimentation. ROS is a middleware for robotics software development and integration. All are compatible with various hardware and software platforms, and can run on multiple operating systems. Choosing the right ML framework can improve the efficiency, effectiveness, and impact of your robotics system, and enhance its functionality, usability, and adaptability in various applications.

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3 How to choose the right ML framework

When it comes to choosing the right ML framework for robotics, there is no single answer. Different frameworks possess different strengths and weaknesses, and thus it is important to consider your project goals and needs when making a decision. To help you make an informed selection, here are some tips to keep in mind: define your ML problem and objectives precisely, research and compare different ML frameworks, test and evaluate them on a small version of your ML problem, and choose the framework that best suits your needs. Additionally, be open to changing or combining frameworks if necessary or beneficial. Consider the trade-offs between performance, usability, flexibility, scalability, and compatibility when selecting a framework.

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RAUNAK CHOUDHARY?? ????

Robopreneur Empowering the next generation to lead India into a tech-driven future ?? Recognized for Excellence in Robotics with State and National Awards
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When choosing a ML framework for robotics, consider your project goals and needs. Define your ML problem and objectives, research and compare different frameworks, test and evaluate them, and choose the best fit. Be open to changing or combining frameworks if necessary. Consider the trade-offs between performance, usability, flexibility, scalability, and compatibility. Choosing the right ML framework can improve the efficiency, effectiveness, and impact of your robotics system, and enhance its functionality, usability, and adaptability in various applications.

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Wiktor Krzeszewski

?? Financing and product development for deeptech ?? | IoT | Smart City | Robotics | AI
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At first define task which should be done. You need information about: - input data - output data - infrastructure(hardware) - user needs(timing, efficiency) - metrics Read research in similar topic to see possible solutions or use your knowledge to narrow selection. Check what constraints is given by infrastructure you will deploy on. For example for more exotic embedded platforms not all of frameworks are available. Prepare small dataset for testing(which can be done on any infrastructure). Based on metrics benchmark frameworks.

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4 Best practices for using ML frameworks

Once you have selected the right ML framework for your robotics project, it is important to follow some best practices to ensure its effective and efficient use. For example, create clean and organized code with consistent naming conventions, coding styles, and comments. Utilize version control and backup systems to track and save your changes, as well as modular and reusable code structures to avoid duplication and confusion. Additionally, take advantage of the framework's documentation, tutorials, examples, and support resources to learn its basics and advanced features. Also, use the framework's tools and utilities to simplify and automate your workflow. This includes its built-in functions, libraries, modules for data preprocessing, model creation, training, evaluation, visualization, debugging, deployment as well as its utilities for distributed computing, parallel processing, and cloud services. Lastly, optimize your ML model and framework for performance and efficiency by utilizing appropriate data formats, types, sizes; model architectures; parameters; hyperparameters; optimization algorithms; learning rates; regularization techniques; hardware platforms; configurations; settings; inference modes such as batch streaming or edge.

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RAUNAK CHOUDHARY?? ????

Robopreneur Empowering the next generation to lead India into a tech-driven future ?? Recognized for Excellence in Robotics with State and National Awards
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To use a ML framework effectively and efficiently in robotics, follow best practices such as creating clean and organized code, utilizing version control and backup systems, using modular and reusable code structures, taking advantage of documentation and resources, and optimizing for performance and efficiency. These practices can improve the quality, reliability, and adaptability of your ML model and framework, and enhance their functionality, usability, and impact in various applications.

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Wiktor Krzeszewski

?? Financing and product development for deeptech ?? | IoT | Smart City | Robotics | AI
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In today's robotics world using ML frameworks is not a magic, it's everyday tool. In many cases expected time to market(or any deployment) is short still with high expectations. Learn how to use already existing trained models and how to fine tune them for your usage. If the project is longer use MLops techniques to ensure that all developed procedures are easy to reproduce and automated.

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5 Future trends and challenges for ML frameworks

ML frameworks for robotics are constantly evolving and improving as new research, technologies, and applications emerge. Future trends and challenges include incorporating more domain knowledge and prior information into ML models, such as physical laws and semantic meanings, to improve accuracy, robustness, and interpretability. Additionally, more efficient and effective learning algorithms, such as meta-learning and multi-task learning, are being developed to enable the model to learn from fewer or more diverse data sources. Furthermore, integrating more modalities and sensors into ML models, such as vision and sound, can enhance the model's perception and interaction capabilities. Finally, ethical and social issues related to ML models must be addressed in order to ensure that the model respects the rights of users and follows the laws of the domains and regions.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Arivukkarasan Raja, PhD

PhD in Robotics | GCC Leadership | Expertise in Enterprise Solution Architecture, AI/ML, Robotics & IoT | Software Application Development | Service Delivery Management | Account Management | Sales & Pre-Sales
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To choose the right machine learning framework for robotics, consider the type of tasks, dataset complexity, real-time performance requirements, and integration with existing software and hardware. Explore popular frameworks like TensorFlow, PyTorch, Keras, and ROS, evaluate features like ease of use, performance, and compatibility with hardware accelerators. Consider ML frameworks that offer seamless integration with Robot Operating System (ROS) and community support resources.

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How can you choose the right ML framework for Robotics?

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1 Criteria for ML frameworks

2 Popular ML frameworks

3 How to choose the right ML framework

4 Best practices for using ML frameworks

5 Future trends and challenges for ML frameworks

6 Here’s what else to consider

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How can you choose the right ML framework for Robotics?

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6

1 Criteria for ML frameworks

2 Popular ML frameworks

3 How to choose the right ML framework

4 Best practices for using ML frameworks

5 Future trends and challenges for ML frameworks

6 Here’s what else to consider

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