Sebastian Wallkotter

Sebastian Wallkotter, Mohamed Chetouani, Ginevra Castellano

Robots that share an environment with humans may communicate their intent using a variety of different channels. Movement is one of these channels and, particularly in manipulation tasks, intent communication via movement is called legibility. It alters a robot's trajectory to make it intent expressive. Here we propose a novel evaluation method that improves the data efficiency of collected experimental data when benchmarking approaches generating such legible behavior. The primary novelty of the proposed method is that it uses trajectories that were generated independently of the framework being tested. This makes evaluation easier, enables N-way comparisons between approaches, and allows easier comparison across papers. We demonstrate the efficiency of the new evaluation method by comparing 10 legibility frameworks in 2 scenarios. The paper, thus, provides readers with (1) a novel approach to investigate and/or benchmark legibility, (2) an overview of existing frameworks, (3) an evaluation of 10 legibility frameworks (from 6 papers), and (4) evidence that viewing angle and trajectory progression matter when users evaluate the legibility of a motion.

Karen Tatarian, Sebastian Wallkotter, Sera Buyukgoz et al.

The use of robots in educational settings is growing increasingly popular. Yet, many of the learning tasks involving social robots do not take full advantage of their physical embodiment. MobiAxis is a proposed learning task which uses the physical capabilities of a Pepper robot to teach the concepts of positive and negative multiplication along a number line. The robot is embodied with a number of multi-modal socially intelligent features and behaviours which are designed to enhance learning. This paper is a position paper describing the technical and theoretical implementation of the task, as well as proposed directions for future studies.

Sebastian Wallkotter, Rebecca Stower, Arvid Kappas et al.

Mind perception in robots has been an understudied construct in human-robot interaction (HRI) compared to similar concepts such as anthropomorphism and the intentional stance. In a series of three experiments, we identify two factors that could potentially influence mind perception and moral concern in robots: how the robot is introduced (framing), and how the robot acts (social behaviour). In the first two online experiments, we show that both framing and behaviour independently influence participants' mind perception. However, when we combined both variables in the following real-world experiment, these effects failed to replicate. We hence identify a third factor post-hoc: the online versus real-world nature of the interactions. After analysing potential confounds, we tentatively suggest that mind perception is harder to influence in real-world experiments, as manipulations are harder to isolate compared to virtual experiments, which only provide a slice of the interaction.

Sebastian Wallkotter, Silvia Tulli, Ginevra Castellano et al.

The issue of how to make embodied agents explainable has experienced a surge of interest over the last three years, and, there are many terms that refer to this concept, e.g., transparency or legibility. One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents. Another reason is that different authors use these terms in different ways. Hence, we review the existing literature on explainability and organize it by (1) providing an overview of existing definitions, (2) showing how explainability is implemented and how it exploits different social cues, and (3) showing how the impact of explainability is measured. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community. This provides the interested reader with an overview of the current state-of-the-art.