After several years of positive projections, some special-purpose domestic robots have finally reached the market, with the IFR Report on Service Robots showing an increase of 25% for domestic and 22% for entertainment robots. This increase is largely due to simple devices, like lawnmowers and vacuum cleaners, but several reports predict a massive market growth in the next years for other companion robots due to new markets in healthcare, rehabilitation, and logistics, fuelled by improvements in the field of Artificial Intelligence (AI). This is challenged by findings from industry (IBM4), which found that, while 82% of enterprises are considering AI for their products, 60% fear liability issues due to a lack of transparency when it comes to decision-making (aka, the “black box” problem). If companies want to sell intelligent robots, novel research solutions for human users’ interpretability and transparency will be key to acceptance. Also, since lay users often have trouble interpreting the current state of a robot, even on a simple level, e.g. to know when a robot is listening or how it is processing the last request. Therefore, an increasingly important issue for the acceptance of robots in human homes is the transparent interpretability of the robot's behaviour and the underlying decision-making processes.
TRAIL - TRAnsparent, InterpretabLe Robots strategically focuses on a novel, highly interdisciplinary and cross-sectoral research and training programme for a better understanding of transparency in deep learning, artificial intelligence, and robotics systems.
To train a new generation of researchers to become experts in the design and implementation of transparent, interpretable neural systems and robots, we have built a highly interdisciplinary consortium, containing expert partners with long-standing expertise in cutting-edge artificial intelligence and robotics, including deep neural networks, computer science, mathematics, social robotics, human-robot interaction and psychology. To build transparent robotic systems, these new researchers or doctoral candidates (DCs) need to learn about the theory and practice of the principles of (1) internal decision understanding and (2) external transparent behaviour. Since the ability to interpret complex robotic systems needs highly interdisciplinary knowledge, we start, at the decision level, to interpret deep neural learning and analyse what knowledge can be efficiently extracted. At the same time, on the behaviour level, the disciplines of human-robot interaction and psychology are key in understanding how to present the extracted knowledge as behaviour intuitively and naturally to a human user to integrate the robot into a cooperative human-robot interaction. A scaffolded training curriculum guarantees that the DCs have not only a deep understanding of both research areas but also experience optimal skill training to be fully prepared for a successful research career in academia and industry.
