At its core, Machine Learning is a subset of Artificial Intelligence that endows computers with the ability to learn data and make informed decisions. In the early days of its advent, Robotics were merely machines that were programmed to perform a trained set of repetitive tasks. However, the integration of Artificial Intelligence technologies with Robotics have brought out a plethora of opportunities to revolutionize diverse fields such as Healthcare, Agriculture, Logistics & Supply Chain etc. This article at OpenGenus aims to shed light on the various applications of Machine Learning in Robotics and some of the fundamental ML concepts used in AI Robots.
Table Of Contents:
- History of Machine Learning in Robotics.
- Applications of Machine Learning in Robotics.
- Machine Learning Concepts used in Robotics.
History of Machine Learning in Robotics
The implementation of Machine Learning has been an ongoing area of research in Robotics. From early 1950s to late 1960s, earliest implementation of AI in Robotics involved rule-based systems to perform a set of specified tasks that did not involve any autonomous learning. However in 1970s, the emergence of improved algorithms such as Neural Networks enabled robots to perform tasks such as navigation and traversal. The introduction of Computer Vision in 2000s revolutionized the field of Artificial Intelligence and robots were now equipped with Robotic Vision which is used in entity detection. Recent advancements include Deep Reinforcement Learning, Generative Adversial Networks (GANs) that have enabled robots to perform complex tasks such as autonomous navigation and natural language processing. The Robotics industry has been steadily advancing over the years and it has been estimated that the sector could be worth as much as $250 billion by 2030.
Applications of Machine Learning in Robotics
Healthcare & Medical Sector
AI and Robotics continue to revolutionize the healthcare sector with their groundbreaking applications that have improved the efficiency of traditional practices, assist medical professionals and enhance patient care.
1. Surgical Assistance Robots- With Computer Vision and AI technologies, surgical assistance robots are continually optimized to achieve high levels of precision and dexterity for performing complex and demanding operations. They are used in micro-surgeries for unclogging blood vessels. They're also used in minimally invasive surgeries in internal procedures like hysterectomy, bariatric surgery where the potential for infection and complications are substantial.
2. Social Robots- These robots interact directly with patients in long-term environment and can analyze the sentiments/emotions of a patient and appropriately engage with them in order to improve a patient's well-being. They're also used to offer assistance and provide directions to visitors.
3. Service Robots- Autonomous Mobile Robots (AMRs) can detect contaminated objects & surfaces and help in their disinfectation by using UVC light. They also aid in hospital room and equipment sanitization by detecting unclean areas.
With the human population growing rapidly each year, the demand for agricultural and food production is skyrocketing as well which has caused food industries and farmeres to turn to turn to Artificial Intelligence and Robotics to introduce new systems and make conventional techniques more efficient.
1. Automated Weeding- Autonomous AI robots equipped with sensors/cameras can detect weeds in an agricultural area and remove unwanted vegetation with no human intervention. Using Visual Recognition Algorithms, these robots can identify weeds and treat them with precision spraying with the right amount of pesticides and fertilizers. In one application, only 20% of the chemical products used in conventional spraying methods were required in AI robots, which significantly reduces negative environmental impact.
2. Soil Analysis- Using Computer Vision tools and Robotics, soil conditions and characteristics such as moisture, consistency, temperature, nutrient level, pH level can be measured and the results are analyzed to get insights for optimum conditions required for the growth of crops and plants.
3. Crop Disease Detection - Autonomous AI Robots are used for monitoring crop health and detect the presence of disease and infections. The information gathered can be subsequently used for the optimization of fertilizer and pesticide application to grow healthier crops.
Logistics and Supply Chain
Over the years, it has been proved that Robots are incredibly adept at organizing and cataloguing products in a production line. It is used in different aspects such as transportation, ordering, packaging, inventory control/management etc.
1. Warehouse Automation- Aerial Drones are especially quick in counting warehouse stocks and updating the inventory management software which can save time. It is proven to reduce labour costs, human error and speed up warehousing processes.
2. Autonomous vehicles: Autonomous vehicles, including trucks, drones, and robots, are being used for transportation and delivery. These vehicles can optimize routes and schedules, reduce labor costs, and improve delivery times.
3. Product Maintenance: AI robots can be used to monitor equipment and predict when maintenance is required. This can reduce downtime and increase productivity.
This type of learning is related to Observational Learning where one learns by observing the behaviours of others. It is used in Military applications, Search & Rescue operations where explicit programming and reinforcement learning can be laborious and time consuming. The AI Robots learns how to perform a task by observing and imitating a human or any other agent.
Machine Learning concepts used in Robotics
It is a Machine Learning training method where a model or an agent where certain behaviours are reinforced through a reward system where the desired outcome or action is rewarded. The model is trained over a period of time using trial & error method. It helps a robot to learn from its environment, adapt to changing conditions and improve itself through a reward mechanism. It is commonly used in path finding/planning where a robot explores a space in order to find the shortest obstacle-free path from a start-point to its end-point.
It is a Machine Learning approach where the models are trained with labelled input data that maps to a labelled output data. This approach is commonly used in object detection. In supply chain and warehouse management, robots can be trained to identify specific products or parts and count them for inventory management and restocking. However, in Robotics, Unsupervised Learning is widely preferred as data is usually complex. Common algorithms used are Decision Trees, Linear Regression, Support Vector Machines and Neural Networks.
It is a Machine Learning approach where a model analyzes and infers patterns from an unlabelled dataset. It identifies hidden patterns and groupings which are not explicitly labelled. In the field of Robotics where the robots and the sensors are exposed to heaps of complex data, unsupervised learning helps in extracting insights and unidentified patterns. Unsupervised learning algorithms can be used to detect anomalies or outliers in data. It can be used in anomaly detection for identifying defective mechanical parts. Common algorithms used are Gaussian Mixture Models, Hidden Markov Models and Hierarchical Clustering.
One of the advancements in Robotics is the introduction of technologies like Robotic Vision that endows a Robot with the ability for visual perception and enables them to interpret visual data from their environment. In social robots, it is used for Gesture Recognition where it can detect and recognize human gestures such as waving and pointing. It is also used in face recognition where it can identify specific individuals and appropriately respond to facial/visual cues.
With this article at OpenGenus, you must have a strong idea of how Machine Learning is used for Robotics.