In a breakthrough for robotics, researchers at the Massachusetts Institute of Technology (MIT) have developed a simpler method for teaching robots how to control their movements. The new approach, which is based on a combination of machine learning and motion planning, could make it easier for robots to learn complex tasks and interact with their environment.
The research team, led by MIT professor and roboticist Daniela Rus, has developed a system that uses machine learning to teach robots how to control their movements. The system combines motion planning, which is the process of determining the best way to move from one point to another, with machine learning, which is the process of teaching a computer to recognize patterns and make decisions.
The team tested the system on a robotic arm and a robotic car. In both cases, the robot was able to learn how to move from one point to another without any prior knowledge of the environment. The team also tested the system on a robotic arm that was tasked with picking up objects from a table. The robot was able to learn how to pick up the objects without any prior knowledge of the task.
The team believes that their system could be used to teach robots how to perform complex tasks such as assembling objects, navigating unfamiliar environments, and interacting with humans. The team also believes that their system could be used to teach robots how to control their movements in a more efficient and accurate way.
The team’s findings have been published in the journal Science Robotics. The research team hopes that their system will make it easier for robots to learn complex tasks and interact with their environment.
https://news.mit.edu/2023/simpler-method-learning-control-robot-0726
