10 ways machine learning is used in robotics

July 9, 2024
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Machine learning in robotics: How it works

Machine learning algorithms allow our robots to detect patterns in huge amounts of data. 

Machine learning gives robots the ability to learn and improve at tasks. It's basically teaching them to get better at things over time, instead of just following pre-programmed instructions.

Robots use sensors like cameras and microphones to collect information from their surroundings. 

Then, they use this data to train machine learning models. These models help robots recognize patterns, make decisions, and take actions based on what they've learned.

10 ways machine learning is used in robotics today

Want to learn more about how artificial intelligence in robotics is making wonderful things happen? 

Let’s get started.

1. Autonomous navigation

How do robots get around on their own? Machine learning helps them navigate without human input.

Check it out: 

  • Advanced environmental mapping: Robots use sensors to map their surroundings, then machine learning algorithms analyze the data to identify walkways, obstacles, and landmarks. The robot can then plan a route to its destination while avoiding crashing into things. 
  • High-level positioning skills. Machine learning also helps robots figure out where they are within a map. By comparing sensor data to the map, the robot can pinpoint its location and correct its course if it gets lost.
  • Path planning. Once a robot knows the layout and its location, machine learning generates the most efficient path to a destination. It can reroute if there are unexpected obstacles or roadblocks along the way.

2. Object recognition

Robots need to identify and classify objects in their environment in order to interact with them. 

There are a few main ways robots use machine learning for object recognition:

  • Image classification. Robots use machine learning algorithms to detect and categorize objects from images and video data. By analyzing thousands of images of different objects, the algorithms learn the visual features of each object and can then predict the class of new images.
  • 3D object detection. Some robots have 3D sensors that provide depth information in addition to images. Machine learning models can go through this 3D data to detect objects, determine their size and position, and track them as they move through the environment.
  • Extract salient features. Machine learning is used to automatically find distinctive features of objects like color, texture, shape, and size. The algorithms determine which features are useful for telling objects apart and recognizing new instances of the same object category.

3. Predictive maintenance

Machine learning is helping robots anticipate and schedule maintenance. 

This is how they do it: 

  • They use data to get ahead of maintenance needs. Robots use machine learning algorithms to sift through data from various sensors that monitor performance and status. With this knowledge, robots can predict when parts are likely to fail.
  • Armed with predictions. Now, robots can schedule maintenance and repairs before problems come up. This proactive approach minimizes unplanned downtime and avoids disruptions.
  • Anticipation means savings. When robots can anticipate issues, replacement parts and repairs can be ordered in advance at a lower cost. This just-in-time model for maintenance helps optimize resources and reduces waste.

4. Human-robot interaction

Human-robot interaction is improving communication and cooperation between humans and robots. 

  • Robots use natural language processing to translate human speech into data they can understand. For example, if you tell a robot, "Please hand me the wrench," it can determine that "wrench" refers to a tool it has in its workspace.
  • Robots use computer vision techniques like object recognition. This allows them to identify tools, parts, and other objects that humans refer to. When a human points to an object across the room and says, "Bring me that," the robot needs to know what "that" is. Computer vision also lets the robot understand gestures and points. 
  • The more robots interact with humans, the more they learn. Machines store memories of these interactions and use them to improve future communications. Over time, robots get better at understanding the nuances of human speech, facial expressions, and body language.
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5. Motion planning

Machine learning algorithms are used to calculate the optimal path for a robot to take. 

Here’s how motion planning helps robots move autonomously: 

  • Adapting to the environment. As robots navigate, sensors collect real-time data, enabling machine learning algorithms to adjust paths for unexpected changes.
  • Trial and error for exploration. Some robots use reinforcement learning, experimenting with movement to map unfamiliar spaces and learn optimal routes.
  • Complex navigation made it possible. Machine learning-powered motion planning lets robots move effectively in dynamic environments like warehouses, factories, and even homes, with self-driving cars being a prime example.

6. Grabbing and manipulation

Robot arms were once limited to repetitive pick-and-place tasks, but machine learning has unlocked their potential for manipulating complex objects.

Here’s how this happens:

  • Robots can now “see” objects using computer vision. This lets them analyze details like size, shape, texture, and position in 3D space. Machine learning helps robots classify objects and determine the best way to grasp them.
  • The right grip for the job.  A robot chooses an appropriate gripper and grasp type for the object using a machine-learning model trained on thousands of examples. This allows the robot to handle everything from heavy boxes to fragile eggs.
  • Planning ahead. The robot plans the precise sequence of movements required to reach out, grasp, and manipulate the object without dropping or damaging it. Machine learning algorithms consider factors like the weight and center of mass of the object.

7. Anomaly detection

Robots use machine learning algorithms to establish a “normal” baseline of data about their environment, and flag changes as “anomalies.” 

This is the way they do it: 

  • Self-diagnosis of hardware issues. Robots can monitor their own performance and detect potential problems like motor or sensor failures.
  • Spotting software glitches. Monitoring changes in programming and logic allows robots to flag unexpected errors or anomalies.
  • Environmental awareness. Robots can detect unfamiliar objects, sounds, or patterns in their surroundings, potentially signaling intrusions or hazards.

8. Quality control

Machine learning in robotics is ideal for inspecting products to elevate quality standards. 

Here’s how that happens: 

  • Scanning for flaws. Machine learning algorithms can be trained on thousands of sample products to learn what constitutes a “good” vs. “defective” item. The robot can then scan new products coming down the assembly line to identify any defects or imperfections.
  • Measuring up. Machine learning also allows robots to take extremely precise measurements of products to check that they meet strict tolerances. The robot might use computer vision to measure the exact dimensions, weight, color, and other attributes of a product to check it’s within acceptable ranges.
  • Keeping improvements flowing. The data from the quality inspections can then be fed back into the manufacturing process to make ongoing improvements. If the robot detects a certain type of defect occurring frequently, the issue can be addressed to improve overall quality and reduce waste. 

9. Energy optimization

Machine learning helps robots optimize their energy usage.

These are the several ways that happens: 

  • Predicting power demands. Algorithms can analyze a robot’s past energy usage to predict how much power it will need for future tasks. The robot can then efficiently allocate energy resources.
  • Optimizing movement. Machine learning techniques like reinforcement learning allow robots to determine the most energy-efficient ways of moving and manipulating objects. Robots learn through trial and error which movements waste the least amount of energy.
  • Adapting to changes. Robots can use machine learning to adapt their energy usage based on environmental changes. For example, a robot may rely more on battery power on a sunny day when solar panels are generating more energy. 

10. Reinforcement learning

Reinforcement learning uses algorithms that allow robots to determine the best ways to achieve goals through trial and error.

This is how the process goes: 

  • Trying and learning. Robots experiment with different actions, receiving rewards for success and penalties for failure.
  • Exploring and optimizing. Through numerous iterations, robots learn the best strategies to maximize rewards, balancing the exploration of new actions with the exploitation of successful ones.
  • AlphaGo's mastery. An example is DeepMind's AlphaGo, which used reinforcement learning to achieve superhuman performance in the complex game of Go, demonstrating the potential of this approach in mastering challenging tasks.

How Standard Bots uses machine learning

Standard Bots is leveraging machine learning in RO1, their flagship product:

  • Learning by doing: RO1 uses machine learning to figure out tasks and get better at them over time.
  • Fixing mistakes on the fly. They use machine learning to adjust to unexpected hiccups on the job, making sure tasks get done right even when things don't go according to plan.
  • Seeing what you can't. Machine learning helps RO1 "see" and understand its surroundings, spotting objects and figuring out how to interact with them — like having an extra pair of eyes on the job.
  • Extra safety features: RO1 uses machine learning and vision to analyze information from its sensors and camera, so it can quickly spot and avoid potential hazards — that includes us squishy humans. 

Summing up 

Machine learning in robotics is not rewriting the rulebook: It’s creating a new one altogether. 

And with this list of 10 ways machine learning is being used, it’s easy to see why. While robots can’t do it all, they’ve been getting nearly exponentially better at doing tasks that were the sole domain of humans.

But where will things go once this emerging technology continues to evolve? No one can say for sure, but you can definitely “color us excited!”

Next steps 

Transform your shop floor with Standard Bots' RO1, a six-axis robotic arm, is engineered to boost efficiency for businesses of any size. 

  • Affordable automation: Invest in the latest robotics technology at half the price of similar models, making automation a reality for your business.
  • Increased productivity: With its impressive 18 kg payload and precise movements, RO1 tackles tasks quickly and accurately, allowing you to produce more in less time.
  • Next-generation intelligence: RO1's built-in AI, comparable to the technology behind ChatGPT, enables it to continuously learn and adapt to your unique workflow. Plus, its intuitive no-code interface makes it a breeze for anyone to program and operate.
  • Prioritizes safety: RO1 is designed to work collaboratively with your team, using state-of-the-art sensors and cameras to avoid accidents and ensure a safe work environment.

Experience differences that RO1 can make firsthand with a 30-day risk-free trial. Contact us to schedule a demo and see how RO1 can empower your business.

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