China has published plans to mass-produce humanoid robots by 2025, as Western companies including Elon Musk's Tesla race to produce their own humanoids. Goldman Sachs has predicted that the market for humanoid robots could be worth $150 billion a year worldwide within 15 years - and that humanoid robots will be viable in factories between 2025-2028 and in other jobs by 2030-2035. The technology will have a positive impact in many fields, believes Marga Hoek, author of Tech For Good, but people need to prepare for it. How will robots change the world of 2035? Hoek said that predictions suggest that up to a quarter of all jobs could be impacted by robotics and AI technology.

People's interactions with machines, from robots that throw tantrums when they lose a colour-matching game against a human opponent to the bionic limbs that could give us extra abilities, are not just revealing more about how our brains are wired – they are also altering them. Emily Cross is a professor of social robotics at the University of Glasgow in Scotland who is examining the nature of human-robot relationships and what they can tell us about human cognition. She defines social robots as machines designed to engage with humans on a social level – from online chatbots to machines with a physical presence, for example, those that check people into hotel rooms. According to Prof. Cross, as robots can be programmed to perform and replicate specific behaviours, they make excellent tools for shedding light on how our brains work, unlike humans, whose behaviour varies. 'The central tenets to my questions are, can we use human-robot interaction to better understand the flexibility and fundamental mechanisms of social cognition and the human brain,' she said.

Industrial robots play an important role in manufacturing process. Since robots are usually set up in parallel-serial settings, breakdown of a single robot has a negative effect on the entire manufacturing process in that it slows down the process. Therefore, fault diagnostic systems based on the internal signals of robots have gained a lot of attention as essential components of the services provided for industrial robots. The current work in fault diagnostic algorithms extract features from the internal signals of the robot while the robot is healthy in order to build a model representing the normal robot behavior. During the test, the extracted features are compared to the normal behavior for detecting any deviation. The main challenge with the existing fault diagnostic algorithms is that when the task of the robot changes, the extracted features differ from those of the normal behavior. As a result, the algorithm raises false alarm. To eliminate the false alarm, fault diagnostic algorithms require the model to be retrained with normal data of the new task. In this paper, domain adaptation, {\it a.k.a} transfer learning, is used to transfer the knowledge of the trained model from one task to another in order to prevent the need for retraining and to eliminate the false alarm. The results of the proposed algorithm on real dataset show the ability of the domain adaptation in distinguishing the operation change from the mechanical condition change.

The age of the robot has been predicted for decades. First used as a term to mean automated labor back in the 1920s, and popularised in the classic 1927 slient movie Metropolis, they have been a regular feature of science fiction ever since. The fact that robots and science fiction go hand in hand – and that predictions that we will soon have robot helpers being regular features of future-gazing since the 1930s – has meant that the idea of robots becoming a central part of our lives have become so familiar that we've come to ignore them. Yet robots have been a reality in manufacturing for decades, having become cost-effective production-line solutions by the 1970s. The Roomba – an automated vacuum cleaner that is perhaps the most famous domestic-helper robot – was launched back in 2002, and has sold more than 10 million units worldwide.

I think the idea of designing robots that look like humans to better interact with humans is a solid "meh." The concept is good, but the execution is usually horrible, and the more your robot tries to look like a human, the more horrible it gets. Having said that, I think that the idea of using robots with specific human features, like eyes, can be a substantial asset for human-robot interaction, if you know what you're doing. Sean Andrist, a PhD student at the University of Wisconsin-Madison (who knows what he's doing), has been researching social gaze with robots. He's developed algorithms that help robots look at people at the right times and in the right ways.