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Machine Learning for Robotics: Roles, Applications, Techniques
Published
20th Nov, 2023
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Today, there are several industrial sectors, like automotive, e-commerce, consumer goods, etc., which use robotics to streamline their operations. Additionally, with the rise of machine learning models, programming robots to identify patterns and effectively apply what they learn has been a revolutionary breakthrough.
This has given rise to machine learning for robotics, thus creating lucrative career options for candidates belonging to data science or computer science. So, if you desire to have a high-flying career in this field, keep reading this blog. We will cover the prospects of machine learning for robotics, enabling you to make an informed choice.
What is Machine Learning for Robotics?
Machine learning in robotics is a discipline in which engineers use machine learning models to program robots. It enables the latter to acquire knowledge from data sets and apply what it learns in order to improve its responsiveness.
ML models are designed to enable robots to collect data from their immediate environment through numerous sensors. Then, they process the information and accordingly optimize their behavior in order to perform their tasks better.
As a result, they learn to identify patterns, gain a better understanding of their environment, and perform certain tasks with higher efficiency. Thus, with time, these robots can continue learning autonomously without needing programming for each new task.
Role of Machine Learning in Robotics
The role of machine learning for robotics is to create a data model that robots can follow to learn how to identify patterns and perform tasks. Such functions can be categorized into four types:
- Data: In this case, developers can use ML models to program robots to analyze data and identify patterns. Thus, when a new data set appears, they can learn how to assess it and perform their tasks without the need for additional programming.
- Vision: With machine learning and other AI applications, robots can process visual data and learn to detect new patterns.
- Motion Control: Machine learning models can be programmed to help robots fine-tune their locomotive parameters. This will help them develop aspects like dynamic interaction, obstacle awareness, etc.
- Grasping: ML models and AI can help robots identify the most powerful position to grasp in a data set.
So, you can see how machine learning is set to revolutionize the field of robotics. Hence, if you wish to have a flourishing career in this emerging sector, you need to have rock-solid basics. In this regard, consider opting for a Machine Learning course for beginners.
Real-world Application of Machine Learning in Robotics
Robotics and machine learning combined play a vital role across several industrial sectors. Some of the essential ones are as follows:
1. Healthcare
In the healthcare sector, ML robots are used by healthcare providers in micro-surgeries as well as complicated operations. They can come in really handy in image-guided tasks, like assisting surgeons in locating cancer locations.
They can analyze large volumes of patient data, detect patterns, make accurate diagnoses, and provide personalized patient care. Additionally, healthcare providers can use them to provide medical aid in remote areas.
2. Agriculture
By using robotic process automation machine learning, agriculturists can significantly scale up their farming activities. For instance, they use robots for performing tasks like seed distribution, harvesting, irrigation, and pest control, thereby drastically reducing their labor costs.
Implementing robots also ensures a high level of precision and increases land potential utility. They can also monitor environmental conservation and land quality enhancement, thereby contributing to the green economy.
3. Warehouses
In warehouses, robots can be programmed using ML models to automate a wide array of tasks. Robots with high-tech sensors like haptics, visual, auditory, and thermal sensors can be trained to operate in a human-like manner, facilitating safety, adaptability, and minimal errors.
Additionally, they can perform complex tasks with high speed and efficiency, which can significantly reduce operational expenses. Companies can also use these robots to perform tasks pulling crates from heights, thus reducing the risk to their workforce.
4. Automobiles
ROS machine learning has extensive usage in the automobile industry. Be it production, designing, or managing the supply chain, companies can program robots with ML models for performing a wide range of activities while maintaining high accuracy.
For example, auto manufacturers can use them for installing fenders and door panels, assembling parts like screws, pumps, engines, etc., painting and coating car parts, and more.
5. Search and Rescue Robotics
Search and rescue robots can be trained using machine learning models to identify casualties, inform them to the controlling authorities, and provide aid. During emergencies, they can be deployed in affected areas for searching trapped people, removing rubble, evacuating casualties, mapping, providing medical treatment, delivering supplies, and more.
These robots can increase the efficiency of search and rescue operations during natural and man-made disasters, along with reducing the risk to human rescue teams.
Apart from these, robotic process automation and machine learning come in handy across several other industrial sectors. However, in all of them, having data science certifications is a must. Check out Data Science course fee and start a certification of your choice today!
Machine Learning Techniques in Robotics
Now, there are several techniques for implementing machine learning for robotics. Here are some of the essential ones:
1. Computer Vision for Robot Perception
Computer vision refers to AI applications that can help computers and ML models analyze data from visual inputs like videos, digital images, etc. This software can help develop robot perception for collecting and processing visual data from their immediate environment and interact with them.
They can be trained to differentiate between several objects, calculate how far they are, determine whether they are moving, and more. Additionally, companies can program them to process thousands of images within minutes. This will help them employ robots in tasks like monitoring production assets, quality checking products, etc.
2. Natural Language Processing for Human-Robot Interaction
As the name suggests, natural language processing allows ML models to interpret and process human languages. This includes analyzing text and speech data to provide responses and execute commands.
As a result, developers can program robots to have conversations with human users, understand user intent and the subtle nuances of languages. It will enable them to interact with the latter in a more naturalistic manner. What’s more, they can perform way more than a set of predetermined tasks and even dynamically adjust their operations based on user requests.
3. Reinforcement Learning for Robot Control
Reinforcement Learning (RL) is a field of machine learning that helps train robots to take suitable action in order to get maximum reward out of a particular situation. Companies can use them to program robotic control systems, which can enable them to train robots to achieve a particular result in a given scenario.
RL can also enable robots to learn from various outcomes and make decisions on what to do next. After performing each task, it receives feedback from human users, which helps them learn whether their choices were correct, incorrect, or neutral.
This helps develop automated systems which can make small decisions without the need for human intervention.
4. Transfer Learning and Fine-tuning in Robotics
Transferring knowledge and find-tuning are deep learning processes that facilitate the transfer of data gained from accomplishing a task for a related problem. This allows other ML models to be trained using the same data, drastically reducing the data requirements along with the amount of computation.
However, this entails using a pre-trained model as a starting point, which will need fine tuning in order to achieve the desired results. By using both these processes, organizations can train robots to perform a wide variety of tasks using the same ML model.
Future Trends in Machine Learning for Robotics
Mentioned below are some of the machine learning for robotics trends that you can expect in the upcoming years:
1. Explainable AI in Robotics
Explainable AI allows model or algorithm developers to look into the underlying codes and fine-tune them to generate desired results. This will enable organizations to program robots for specific requirements, as per the needs of clients.
Access to the AI's codes will also allow them to determine the accuracy of the ML model's findings, making it more transparent and trustworthy.
2. Swarm Robotics and Multirobot Systems
Swarm robotics and multi-robot systems are frameworks that allow a group of robots to work on a well-defined goal. They are flexible, scalable, and fault-tolerant systems that can be useful for several applications like simultaneous vehicle transportation, delivering payloads, military surveillance missions, locating survivors in disaster-hit areas, etc.
3. Human-Robot Collaboration
Human-robot collaboration entails humans and robots working together as a team towards a common goal. They can coordinate complex shared tasks where robots can work autonomously to carry out tasks based on specific commands. On the other hand, humans can oversee their work and take manual decisions when need be.
An excellent example in this case can be of military robots. They are deployed to clear obstacles, which can act as cover for enemies and detect hazards like improvised explosive devices (IEDs).
4. Quantum Machine Learning for Robotics
Quantum machine learning is an amalgamation of quantum computing and machine learning. Engineers can use these models to program robots for analyzing complex data at high speeds. They can even identify nanoscale aberrances and particles, serving numerous use cases in material sciences and meteorology.
Quantum machine learning and robotic process automation software can come in very handy in developing voice and facial recognition software for medical robots, conducting gas analysis to prevent toxic leaks, and more.
Conclusion
Given the increasing implementation of machine learning for robotics, in the coming years, there will be a high demand for machine learning professionals. Thus, to ensure that you have the necessary skills to leverage the situation, having the appropriate certifications is a must.
In this regard, going for KnowledgeHut's Machine Learning course for beginners can be a smart choice. Our instructor-led sessions and case-study-based discussions will provide you with both practical and technical knowledge, which can help you ace the certification exam.
Frequently Asked Questions (FAQs)
1. How is machine learning used in robotics?
Machine learning helps robots identify patterns, analyze their environments, and apply what they learn to perform their tasks better. This enables them to work autonomously, reducing the need for programming in order to provide every single task.
2. What is an example of machine learning in robotics?
Machine learning can help program robots in the automobile industry. Companies can program them to assemble components, install parts, apply paint, and more.
3. Do robotics engineers use machine learning?
Robotics engineers use machine learning models to train robots so that they can identify data patterns, learn from them, and execute their tasks with higher efficiency.
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