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Machine Learning Technologies used in Robotics
- Computer Vision: In this field of robotics, machine learning is used to improve and adapt camera based vision. Areas, where these intelligent camera systems are used are for example autonomous driving, intelligent drones, smart video surveillance (automatically calling police when people are fighting), smart home camera systems (opening the door when you are approaching). As it is impossible to teach a Tesla every possible situation in traffic, the solution is to create an intelligent system, in which one Tesla learns to handle a new situation and teaches it to all others. This is what ML in computer vision is all about.
- Assistive and imitation learning: This is a new arising field in robotics, in which we teach or use robotics in every day use. Assistive robotics means, that we take the robot arm by the hand and show them what to do. This is helpful, when you are too shaky to do tiny work or to weak to lift heavy items. The imitation learning is based on this, it is when you teach your robot how to move, and it repeats this movement after you. This way of supervised learning is great for frequent changes in movement, as it only takes a few minutes to teach the robot a new routine. Imitation learning is closely related to observational learning, a behavior exhibited by infants and toddlers.
- Medical Robotics: A very important field in new robotics, as it will enable more people to access medical help faster. Again, these robots will often be confronted with new situations, so learning and adapting is crucial. This is where ML comes in, to help analyse and predict situations based on learned data. An assistive robot is a device that can sense, process sensory information, and perform actions that benefit people with disabilities and seniors. Movement therapy robots provide a diagnostic or therapeutic benefit. Both of these are technologies that are largely still confined to the lab, as they're still cost-prohibitive for most hospitals in the U.S. and abroad.