SLOT-V: Supervised Learning of Observer Models for Legible Robot Motion Planning in Manipulation

We present SLOT-V, a novel supervised learning framework that learns observer models (human preferences) from robot motion trajectories in a legibility context. Legibility measures how easily a (human) observer can infer the robot's goal from a robot motion trajectory. When generating such trajectories, existing planners often rely on an observer model that estimates the quality of trajectory candidates. These observer models are frequently hand-crafted or, occasionally, learned from demonstrations. Here, we propose to learn them in a supervised manner using the same data format that is frequently used during the evaluation of aforementioned approaches. We then demonstrate the generality of SLOT-V using a Franka Emika in a simulated manipulation environment. For this, we show that it can learn to closely predict various hand-crafted observer models, i.e., that SLOT-V's hypothesis space encompasses existing handcrafted models. Next, we showcase SLOT-V's ability to generalize by showing that a trained model continues to perform well in environments with unseen goal configurations and/or goal counts. Finally, we benchmark SLOT-V's sample efficiency (and performance) against an existing IRL approach and show that SLOT-V learns better observer models with less data. Combined, these results suggest that SLOT-V can learn viable observer models. Better observer models imply more legible trajectories, which may - in turn - lead to better and more transparent human-robot interaction

A Preliminary Case Study on Gender Norms in Robot-Assisted Diagnosis of Perinatal Depression : A Socio-Legal HRI Perspective

We propose that an interdisciplinary approach is necessary to better understand the normative gender aspects of automating perinatal depression-care in socially assistive robots and how to take them into account at design stage. Consequently, the aim is twofold: firstly, to understand the existing normative gender aspects within perinatal depression, and secondly, to enable the socially assistive robot to “normatively adapt” to the patient. Both, we argue, are vital in order to automate trustworthy perinatal depression assistance within socially assistive robots, as well as overcome the current social prejudices attached to the condition

How Expressiveness of a Robotic Tutor is Perceived by Children in a Learning Environment

We present a study investigating the expressiveness of two different types of robots in a tutoring task. The robots used were i) the EMYS robot, with facial expression capabilities, and ii) the NAO robot, without facial expressions but able to perform expressive gestures. Preliminary results show that the NAO robot was perceived to be more friendly, pleasant and empathic than the EMYS robot as a tutor in a learning environment

Adaptive Robotic Tutors that Support Self-Regulated Learning::A Longer-Term Investigation with Primary School Children

Robots are increasingly being used to provide motivating, engaging and personalised support to learners. These robotic tutors have been able to increase student learning gain by providing personalised hints or problem selection. However, they have never been used to assist children in developing self regulated learning (SRL) skills. SRL skills allow a learner to more effectively self-assess and guide their own learning; learners that engage these skills have been shown to perform better academically. This paper explores how personalised tutoring by a robot achieved using an open learner model (OLM) promotes SRL processes and how this can impact learning and SRL skills compared to personalised domain support alone. An OLM allows the learner to view the model that the system holds about them. We present a longer-term study where participants take part in a geography-based task on a touch screen with adaptive feedback provided by the robot. In addition to domain support the robotic tutor uses an OLM to prompt the learner to monitor their developing skills, set goals, and use appropriate tools. Results show that, when a robotic tutor personalises and adaptively scaffolds SRL behaviour based upon an OLM, greater indication of SRL behaviour can be observed over the control condition where the robotic tutor only provides domain support and not SRL scaffolding

Expressive Copying Behavior for Social Agents: A Perceptual Analysis

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Gender Fairness in Social Robotics : Exploring a Future Care of Peripartum Depression

In this paper we investigate the possibility of socially assistive robots (SARs) supporting diagnostic screening for peripartum depression (PPD) within the next five years. Through a HRI/socio-legal collaboration, we explore the gender norms within PPD in Sweden, to inform a gender-sensitive approach to designing SARs in such a setting, as well as governance implications. This is achieved through conducting expert interviews and qualitatively analysing the data. Based on the results, we conclude that a gender-sensitive approach is a necessity in relation to the design and governance of SARs for PPD screening