Purpose of Review To assess the state-of-the-art in research on trust in robots and to examine if recent methodological advances can aid in the development of trustworthy robots. Recent Findings While traditional work in trustworthy robotics has focused on studying the antecedents and consequences of trust in robots, recent work has gravitated towards the development of strategies for robots to actively gain, calibrate, and maintain the human user's trust. Among these works, there is emphasis on endowing robotic agents with reasoning capabilities (e.g., via probabilistic modeling). Summary The state-of-the-art in trust research provides roboticists with a large trove of tools to develop trustworthy robots. However, challenges remain when it comes to trust in real-world human-robot interaction (HRI) settings: there exist outstanding issues in trust measurement, guarantees on robot behavior (e.g., with respect to user privacy), and handling rich multidimensional data. We examine how recent advances in psychometrics, trustworthy systems, robot-ethics, and deep learning can provide resolution to each of these issues. In conclusion, we are of the opinion that these methodological advances could pave the way for the creation of truly autonomous, trustworthy social robots.

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Many older adults hold powerful positions in governments and corporate boards throughout the world. Accordingly, older adults often have to make important financial decisions on behalf of others under risk. Although it is common to observe younger adults taking more risks when making financial decisions for others, it is unclear if older adults exhibit the same self-other discrepancies. Here, we conducted 2 studies (88 and 124 participants, respectively) to examine self-other discrepancies in financial decision making under risk in older adults. We focused on 3 aspects of financial decision making: loss aversion (a tendency to weight potential losses more strongly than potential gains), risk-aversion asymmetry (a tendency to be risk-averse for potential gains and risk-seeking for potential losses), and risk preferences separately in gain and loss domains. Using computational modeling and behavioral economics tasks, we found weaker self-other discrepancies in older adults (compared with younger adults) across all 3 aspects. We also replicated the age differences in self-other discrepancies in loss aversion across 2 largely nonoverlapping cohorts. Thus, it appears that when making financial decisions on behalf of others, older adults, relative to younger adults, have a stronger disposition to regard others' financial outcomes as important as their own. (PsycINFO Database Record

Common experience suggests that agents who know each other well are better able to work together. In this work, we address the problem of calibrating intention and capabilities in human-robot collaboration. In particular, we focus on scenarios where the robot is attempting to assist a human who is unable to directly communicate her intent. Moreover, both agents may have differing capabilities that are unknown to one another. We adopt a decision-theoretic approach and propose the TICC-POMDP for modeling this setting, with an associated online solver. Experiments show our approach leads to better team performance both in simulation and in a real-world study with human subjects.