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A pioneering artificial intelligence and robotics company from China has developed a repository of over 1 million robotic movements to provide robotic training.
Robot learning is a research field at the intersection of machine learning and robotics. It studies techniques allowing a robot to acquire novel skills or adapt to its environment through learning algorithms.
Inverse reinforcement learning (IRL) is the process of deriving a reward function from observed behavior. While ordinary "reinforcement learning" involves using rewards and punishments to learn behavior, in IRL the direction is reversed, and a robot observes a person's behavior to figure out what goal that behavior seems to be trying to achieve. [3]
On-the-job training (widely known as OJT) is an important topic of human resource management. It helps develop the career of the individual and the prosperous growth of the organization. On-the-job training is a form of training provided at the workplace. During the training, employees are familiarized with the working environment they will ...
Robotics engineers design robots, maintain them, develop new applications for them, and conduct research to expand the potential of robotics. [2] Robots have become a popular educational tool in some middle and high schools, as well as in numerous youth summer camps, raising interest in programming, artificial intelligence and robotics among ...
Robotics careers are widely predicted to grow in the 21st century, as robots replace more manual and intellectual human work. Some workers who lose their jobs to robotics may be well-placed to retrain to build and maintain these robots, using their domain-specific knowledge and skills.
Robotic process automation (RPA) is a form of business process automation that is based on software robots (bots) or artificial intelligence (AI) agents. [1] RPA should not be confused with artificial intelligence as it is based on automotive technology following a predefined workflow. [2]
As developmental robotics is a relatively new research field and at the same time very ambitious, many fundamental open challenges remain to be solved. First of all, existing techniques are far from allowing real-world high-dimensional robots to learn an open-ended repertoire of increasingly complex skills over a life-time period.