Artificial intelligence (AI) technology plays a crucial role in advancing robots and making them more intelligent, adaptable, and capable of performing complex tasks. Here are some ways AI can be utilized to build more advanced robots:



1. **Machine Learning Algorithms:** Implement machine learning algorithms to enable robots to learn from data and improve their performance over time. This can be particularly useful in tasks where the environment is dynamic and constantly changing.


2. **Computer Vision:** Integrate computer vision techniques to allow robots to perceive and understand their surroundings. This technology enables robots to identify objects, people, and obstacles, which is essential for tasks such as autonomous navigation and object manipulation.


3. **Natural Language Processing (NLP):** Implement NLP algorithms to enable robots to understand and respond to human speech. This capability is valuable in human-robot interaction scenarios, such as customer service or assistance for the elderly and people with disabilities.


4. **Reinforcement Learning:** Use reinforcement learning algorithms to enable robots to learn optimal actions by receiving feedback from their environment. This approach is particularly useful in tasks where the robot has to make a sequence of decisions to achieve a goal, such as game playing or robotic control tasks.


5. **Sensor Fusion:** Combine data from various sensors, such as cameras, lidar, and gyroscopes, using AI techniques. Sensor fusion enhances the robot's perception capabilities, allowing it to create a more comprehensive and accurate understanding of its environment.


6. **Predictive Analytics:** Utilize AI-powered predictive analytics to anticipate future events or actions based on historical data. In the context of robotics, this can be applied to predict equipment failures, plan efficient routes for delivery robots, or anticipate human behavior in social robots.


7. **Human-Robot Collaboration:** Develop AI algorithms that enable robots to collaborate effectively with humans. This includes understanding human gestures, emotions, and intentions, allowing for safer and more intuitive interaction between humans and robots in shared environments.


8. **Autonomous Navigation:** Implement AI algorithms for simultaneous localization and mapping (SLAM) to enable robots to create maps of their environments and navigate autonomously. This is crucial for applications such as self-driving cars, drones, and warehouse robots.


9. **Cognitive Computing:** Integrate cognitive computing technologies that allow robots to simulate human thought processes. This includes capabilities such as problem-solving, reasoning, and decision-making, enabling robots to handle complex tasks in diverse environments.


10. **Ethical AI:** Develop AI systems for robots that adhere to ethical principles and guidelines. This includes ensuring robots make ethical decisions, respect privacy, and operate safely in human environments.


By combining these AI technologies, researchers and engineers can create robots that are not only more advanced but also safer, more efficient, and better suited for various real-world applications.


Which fields of AI are used in robotics?


You must have seen videos where a robot is asked to bring water and the robot finds it’s way toward the kitchen, takes an empty glass, fills up water and then brings it to the person who requested it.


This is one of the simpler examples where arguably the hottest field of AI, called Reinforcement Learning, is used along with robotics.


The key to using this AI front as opposed to others is that Reinforcement Learning, is in fact, very intuitive to how we humans learn. It works on the concept of mapping situations to actions so as to maximise a numerical “reward” signal. To put something as mathematically rigorous as Reinforcement Learning down lucidly, I’ll give you an example of a human learning to ride a bicycle. If you think about it, we have never really been “taught” how to ride a bicycle. We just fell down a few times and well, over time, figured it out. The times we fell down, were our “rewards” with a negative value and the times we didn’t, it was a “positive” reward and in the end, we ended up learning how to ride. Reinforcement Learning is exactly the same paradigm.


Another very hot field in AI research is Reinforcement Learning taken one step further: Deep Reinforcement Learning. Here, even the reward signals are self-attained by the machine as opposed to RL where we define rewards for the set of actions. This means that the robot “learns through interaction and exposure”.


I should mention that AI fronts like Natural Language Processing and Computer Vision are also used with Reinforcement Learning within the same problem. Going back to the example of a robot fetching you a glass of water, in order to successfully complete the task, it first has to understand that you said “I want a glass of water” or “Fetch me a glass of water” (and also that these two sentences mean the same thing!). Next, it has to “see” with robotic eyes and find its way to the kitchen, spot a glass of water, pick it up, watch the water level while filling the glass up and then locate you on the return journey. All of that is Computer Vision.


So to conclude, combinations of Reinforcement Learning, Deep Reinforcement Learning, Natural Language Processing and Computer Vision is used in Robotics.