Robot appearance crucially shapes Human-Robot Interaction (HRI) but is typically described via broad categories like anthropomorphic, zoomorphic, or technical. More precise approaches focus almost exclusively on anthropomorphic features, which fail to classify robots across all types, limiting the ability to draw meaningful connections between robot design and its effect on interaction. In response, we present METAMORPH, a comprehensive framework for classifying robot morphology. Using a metamodeling approach, METAMORPH was synthesized from 222 robots in the IEEE Robots Guide, offering a structured method for comparing visual features. This model allows researchers to assess the visual distances between robot models and explore optimal design traits tailored to different tasks and contexts.

This study explores which factors of the visual design of a robot may influence how humans would place it in a collaborative cooking scenario and how these features may influence task delegation. Human participants were placed in a Virtual Reality (VR) environment and asked to set up a kitchen for cooking alongside a robot companion while considering the robot's morphology. We collected multimodal data for the arrangements created by the participants, transcripts of their think-aloud as they were performing the task, and transcripts of their answers to structured post-task questionnaires. Based on analyzing this data, we formulate several hypotheses: humans prefer to collaborate with biomorphic robots; human beliefs about the sensory capabilities of robots are less influenced by the morphology of the robot than beliefs about action capabilities; and humans will implement fewer avoidance strategies when sharing space with gracile robots. We intend to verify these hypotheses in follow-up studies.

We present the Meta-Ontology for Introspection (MOI): Inspired by fundamental processes of the human mind, cognitive architectures (CAs) explore ever more methods to leverage metacognition. Still, an ontological model to trace metacognitive experiences for learning or as input for metacognitive control routines has yet to be developed. Based on a review of existing standards, we formally identify the relevant scope in the form of Competency Questions (CQs) and extend SOMA, a well-established formal ontology initially designed to interpret episodic memories of a robotic CA. The resulting MOI can model a CA’s software and capabilities of single components, trace information processing and inter-component communication, label self-lived mental events, and capture causal relationships. We evaluate MOI via the CQs and exemplarily demonstrate its reasoning capabilities.

Circumscription is one of the main approaches for defining non-monotonic description logics (DLs) and the decidability and complexity of traditional reasoning tasks, such as satisfiability of circumscribed DL knowledge bases (KBs) are well understood. For evaluating conjunctive queries (CQs) and unions thereof (UCQs), in contrast, not even decidability had been established. In this paper, we prove decidability of (U)CQ evaluation on circumscribed DL KBs and obtain a rather complete picture of both the combined complexity and the data complexity, for DLs ranging from ALCHIO via EL to various versions of DL-Lite. We also study the much simpler atomic queries (AQs).