Name: Anna Mura, Vicky Vouloutsi
A cognitive architecture for social robots to exchange knowledge about the world and the self.
Society more than ever needs robots performing side by side with humans in different environments and levels of operation, especially in industry and for environmental sustainability.
In our collective imagination, we are prepared to accept “social” robots that can help us to supply those societal/industrial needs that are no longer undertaken by humans. However, we are still far from having cognizant (mindful) robots that understand our needs, and we can trust. Remarkedly, very little is done to transfer principles of perception of the self and the others to autonomous robots that are intended to work side by side with humans. Why is that?
The fundamental problem scientists and engineers struggle with is how much of our understanding of how the mind operates must be transferred to the machine, and how? [Verschure, 2016; Lallee, 2015]. According to the literature, there are two approaches grounded on cognitive architecture that focus on the making of socially interactive robots [T. Fong 2003]: “Functionally- driven robots” where design and functionality are based more on the robot’s “social intelligence appearance” rather than a science-based design. These robots may not require a deep understanding of how the mind operates to build competent robots, i.e., assistive anthropomorphic robots [J. Pineau 2003]; Biologically-grounded robots that are based on theories of natural and social sciences and thus more connected to humans, as they may function using similar principles of perception, decision making, and empathy.
Nevertheless, and in spite of great advances made in the last decade towards solving the worker–robot interaction endeavor in HRI, very few robots work in industry side by side with humans in a collaborative/social manner. And it is clear that developing a cognitive/social architecture to guides our interactions with robots is more critical than previously thought.
The HR-Recycler Project, funded by the H2020 Program of the EU under GA 820742 addresses this challenge by developing a hybrid collaboration environment, where humans and robots will share and undertake at the same time different processing and manipulation tasks, targeting the industrial application of Waste Electrical and Electronic Equipment (WEEE) recycling.
HR-Recycler will advance “Social robotics for safe Human-Robot Collaboration” by capitalizing on the Distributed Adaptive Control (DAC) cognitive architecture [Verschure 2012]. DAC includes a motivation system for social interaction that drives communication and is based on self-regulation and autonomy, and high-level cognitive functions [Lallee 2015, C. Moulin Frier 2017 ].