Machine learning (ML) and robotics are intersecting in a way that pushes the boundaries of what automated systems can do. The fusion of machine learning with robotics creates more adaptable robots that can perform complex tasks, contributing to advancements in various industries and everyday life. By leveraging machine learning, robots can work efficiently and flexibly in changing environments, which is crucial for tasks ranging from manufacturing to healthcare.

What is Machine Learning (ML)?

Machine learning (ML) is a type of artificial intelligence (AI). AI is a broad field of computer science focused on creating systems capable of performing tasks that include problem-solving, recognition, understanding natural language, and decision-making. AI systems can be rule-based, using a predefined set of rules, or they can learn from data.

ML is a subset of AI that involves developing algorithms that enable computers to learn from and make predictions or decisions based on data. Unlike rule-based AI, ML is specifically concerned with developing algorithms that can change their performance or improve their accuracy as they are exposed to more data over time.

How Machine Learning is Used in Robotics

Machine Vision

We’ve already looked at machine vision, but it is essential to point out that it is an important ML tool. Machine vision refers to technology and methods used to provide image-based analysis for applications such as automated inspection, quality control, and robot guidance. In machine vision, ML algorithms are used to interpret the visual data captured by the machine vision systems to extract patterns, make decisions, or perform actions based on the visual information. ML enables machine vision systems to adapt to new scenarios without being explicitly programmed for them. This is particularly useful in dynamic environments where conditions can change rapidly.

Industrial Robots

ML in industrial robots, connected to the industrial Internet of Things (IIoT), is transforming the landscape of manufacturing and production. Industrial robots equipped with ML algorithms can predict when maintenance is needed—rather than relying on traditional scheduled maintenance—by analyzing sensor data and other indicators of equipment failure, which ultimately reduces costs related to downtime.

Moreover, ML-equipped industrial robots can adjust their actions in real time to accommodate variations in materials or environmental conditions, such as temperature, speed, and pressure.

Collaborative Robots (Cobots)

Cobots use ML to work safely alongside humans, learning from their movements and adapting to better assist them. They can learn to anticipate human actions and adjust their behavior to avoid collisions and optimize cooperative tasks. Due to advances in machine vision and smart grippers, cobots are expanding into new applications, like spot welding. In actuality, they can assist with tasks that can be challenging for humans, such as precise application of paint or adhesives or ergonomically challenging tasks.

Where We See Machine Learning in Real Life


In manufacturing, ML-powered robots are making significant strides in optimizing assembly lines and enhancing production quality. For example, auto manufacturers can now assemble different car models on the same production line without lengthy setup times or manual reprogramming. As we saw above, ML-equipped robots also improve precision in tasks like assembling, painting, and welding. Automation systems with machine learning use machine vision and real-time data analytics to detect and adapt to changes in their environment, ensuring consistent quality and reducing the rate of defects.


ML in healthcare, particularly in the context of surgical robots, is a rapidly advancing field that offers numerous benefits. ML algorithms can enhance the precision of surgical robots, allowing them to perform complex procedures with greater control and less invasiveness than traditional surgery. Surgical robots equipped with ML can also process vast amounts of medical data to identify patterns and insights that can assist surgeons in training and making more informed clinical decisions.


ML is playing a transformative role in warehousing, particularly through the use of robots for inventory management and order fulfillment. Robots with ML capabilities can automate routine tasks such as picking, sorting, and palletizing goods. They can also provide insights into inventory levels, stock availability, shipment timeframes, and fulfillment rates, enhancing operational efficiency.

Implementing Machine Learning in Your Manufacturing Process

Manufacturers looking to introduce ML into their automated manufacturing process can start by identifying use cases where ML can add value and integrate into already-existing systems. For example, at what moments during production would adding machine vision facilitate quality control? Where might cobots assist human workers in process optimization? What are common variables in the manufacturing process that would be helpful if robots could navigate?

The best automation integrations are designed with scalability in mind so that it can grow and adapt as manufacturing needs evolve. Here at Wes-Tech, we try to evaluate all possible automation projects with an eye for what is possible but also what is practical. If you haven’t yet, check out the eight important criteria we identified for industrial automation ROI.

Contact us for a free consultation to explore how ML can be part of your automation solution.