Claire chatted to Lerrel Pinto from New York University about using machine learning to train robots to adapt to new environments. Lerrel Pinto is an Assistant Professor of Computer Science at New York University (NYU). His research is aimed at getting robots to generalize and adapt in the messy world we live in. His lab focuses broadly on robot learning and decision making, with an emphasis on large-scale learning (both data and models); representation learning for sensory data; developing algorithms to model actions and behaviour; reinforcement learning for adapting to new scenarios; and building open-source, affordable robots.

Well they are, but not quite as remorselessly or as swiftly as the movies might have conditioned us to imagine. And when the robot age does arrive, the impact on New Zealand -- in jobs and economic disruption -- may not be as apocalyptic as some future scenarios imagine. At least that is the position of two leading robotics researchers, Armin Werner of Lincoln Agritech, and Bruce MacDonald, an Auckland University computer engineering specialist with over 30 years' skin in the robot game. Werner, whose background is in precision agriculture that will have its most advanced developments in robotics, believes the broad use of automation will create more jobs, at least at the skilled end of the labour market. His view is that the embrace of technology will lead to different forms of work, rather than making work more difficult.

A series of new, origami-like suits turn robots into real-life'Transformers' that can walk, sail, roll or glide. The plastic suits start out flat, but when heated fold over the robot to transform it into a specialised machine. Researchers suggest their adaptable suit-wearing robots could one day be used to build space colonies remotely. A series of new, origami-like suits can turn robots into real-life'transformers' that can walk, sail, roll or glide. The small Primer robots are made of lead and controlled remotely using magnetic fields.

Researchers have developed robots that learn to live with damaged parts in less than a minute, instead of the many hours needed by traditional self-learning systems. The system paves the way for robots to be used in a wide variety of settings, coping with damage that occurs in the real world. Possible applications include robots looking after the elderly, rescuing earthquake victims or doing housework. We marvel at the robots we see in films: some try to wipe out humanity, such as Ultron in the recent Avengers film, while others like C-3PO in Star Wars are helpful albeit slightly annoying. But they are the still the stuff of science fiction, partly because if the slightest thing goes wrong with a real-life robot it usually stops working altogether.

This paper describes our ongoing research effort to explore how personality types factor into HRI; in particular, the degree of patience a person has when teaching an error-prone robot in a learning from demonstration setting.Our goal is to establish personality metrics that will ultimately allow for the design of algorithms that automatically tune robot behavior to best suit user preferences based on personality.