Leolani: a reference machine with a theory of mind for social communication

Our state of mind is based on experiences and what other people tell us. This may result in conflicting information, uncertainty, and alternative facts. We present a robot that models relativity of knowledge and perception within social interaction following principles of the theory of mind. We utilized vision and speech capabilities on a Pepper robot to build an interaction model that stores the interpretations of perceptions and conversations in combination with provenance on its sources. The robot learns directly from what people tell it, possibly in relation to its perception. We demonstrate how the robot's communication is driven by hunger to acquire more knowledge from and on people and objects, to resolve uncertainties and conflicts, and to share awareness of the per- ceived environment. Likewise, the robot can make reference to the world and its knowledge about the world and the encounters with people that yielded this knowledge.Comment: Invited keynote at 21st International Conference on Text, Speech and Dialogue, https://www.tsdconference.org/tsd2018

Investigating cooperation with robotic peers

We explored how people establish cooperation with robotic peers, by giving participants the chance to choose whether to cooperate or not with a more/less selfish robot, as well as a more or less interactive, in a more or less critical environment. We measured the participants' tendency to cooperate with the robot as well as their perception of anthropomorphism, trust and credibility through questionnaires. We found that cooperation in Human-Robot Interaction (HRI) follows the same rule of Human-Human Interaction (HHI), participants rewarded cooperation with cooperation, and punished selfishness with selfishness. We also discovered two specific robotic profiles capable of increasing cooperation, related to the payoff. A mute and non-interactive robot is preferred with a high payoff, while participants preferred a more human-behaving robot in conditions of low payoff. Taken together, these results suggest that proper cooperation in HRI is possible but is related to the complexity of the task

Impacts of multimodal feedback on efficiency of proactive information retrieval from task-related HRI

Gonsior B, Landsiedel C, Mirnig N, et al. Impacts of multimodal feedback on efficiency of proactive information retrieval from task-related HRI. Journal of Advanced Computational Intelligence and Intelligent Informatics (Special Issue on Cognitive Infocommunications). 2012;16(2):313-326

Reducing Disaster Risk to Life and Livelihoods by Evaluating the Seismic Safety of Kathmandu’s Historic Urban Infrastructure: enabling an interdisciplinary pilot

Kathmandu’s cities are exceptional architectural and artistic achievements, underpinned by centuries of seismic adaptation. They represent portals where heavens touch the earth and individuals commune with guiding deities; their tangible and intangible values promoting community cohesion. Kathmandu’s skyline was dramatically altered by the 2015 Gorkha Earthquake as almost 9,000 people died. Hundreds of monuments were damaged or collapsed, resulting in the cancelling of 32 per cent of tourist visits, a major GDP source. Following ODA pledges of US$2.5 billion, Nepal’s Government approved the rehabilitation of many but there are tensions between interpretations of Sendai’s ‘Build Back Better’ framework and the preservation of the authenticity of Kathmandu’s UNESCO World Heritage Site. Our interdisciplinary North–South partnership piloted the integration of archaeology and geoarchaeology with 3D visualisation, geotechnical and structural engineering to co-produce methodologies to evaluate and improve the seismic safety of historic urban infrastructure, reducing direct risk to life and livelihoods, while respecting and preserving authenticity and traditions and, in some cases, revitalising them