Creating 3D assets is essential for game development. Modeling requires time and technical skills, and fast prototyping is challenging, especially for novice designers. To make complex 3D modeling more accessible while strengthening creativity, we present CreatureCreator, a tool that enables users to generate 3D models from an image of a created artifact. We investigated two input modalities: drawing with pen and paper and sculpting with clay. Adding an analog input modality to the generation process could lower the entry barrier for diverse user groups, enabling intuitive and creative modeling. In a within-subject study with game design-experienced participants (n=20), both modalities scored highly for user experience, engagement, and creativity support, with the clay input especially valued for its generated models and enhanced sense of control. The interaction workflow supports playful exploration by allowing users to experiment with parameters and using AI as a production and/or ideation partner.

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.

Affordances - i.e. possibilities for action that an environment or objects in it provide - are important for robots operating in human environments to perceive. Existing approaches train such capabilities on annotated static images or shapes. This work presents a novel dataset for affordance learning of common household tasks. Unlike previous approaches, our dataset consists of video sequences demonstrating the tasks from first- and third-person perspectives, along with metadata about the affordances that are manifested in the task, and is aimed towards training perception systems to recognize affordance manifestations. The demonstrations were collected from several participants and in total record about seven hours of human activity. The variety of task performances also allows studying preparatory maneuvers that people may perform for a task, such as how they arrange their task space, which is also relevant for collaborative service robots.

In this explorative study, we investigated how a zoomorphic dog-like robot could encourage users to engage in active behavior through nudges with different levels of intrusiveness. We examined three hypotheses on the effectiveness of different intensity levels, the impact of zoomorphic design on emotional responses, and the influence of prior dog experience on interactions. Using a within-subject approach with 34 participants, we found that low-intensity nudges were most sufficient at encouraging active behavior, contradicting our first hypothesis. Participants generally responded positively to the zoomorphic design, supporting our second hypothesis. Our third hypothesis was approved as participants with prior dog experience perceived the robot as less intrusive and reported higher user experience. This finding was complemented by these specific users putting a powerful mental model of a real dog directly on our robot dog, sometimes even resulting in projecting attributes like "thirst" or other desires to the robot. We call this new finding "Fluffy Tightrope" - the balancing act between realism and abstraction in designing a zoomorphic robot, so that users with animal experience respond to nudges, but do not project too strong wishes and desires onto the robot resulting in misinterpretations.

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.

This paper introduces a novel benchmark that has been designed as a test bed for evaluating whether artificial agents are able to understand how to perform everyday activities, with a focus on the cooking domain. Understanding how to cook recipes is a highly challenging endeavour due to the underspecified and grounded nature of recipe texts, combined with the fact that recipe execution is a knowledge-intensive and precise activity. The benchmark comprises a corpus of recipes, a procedural semantic representation language of cooking actions, qualitative and quantitative kitchen simulators, and a standardised evaluation procedure. Concretely, the benchmark task consists in mapping a recipe formulated in natural language to a set of cooking actions that is precise enough to be executed in the simulated kitchen and yields the desired dish. To overcome the challenges inherent to recipe execution, this mapping process needs to incorporate reasoning over the recipe text, the state of the simulated kitchen environment, common-sense knowledge, knowledge of the cooking domain, and the action space of a virtual or robotic chef. This benchmark thereby addresses the growing interest in human-centric systems that combine natural language processing and situated reasoning to perform everyday activities.

Situationally-aware artificial agents operating with competence in natural environments face several challenges: spatial awareness, object affordance detection, dynamic changes and unpredictability. A critical challenge is the agent’s ability to identify and monitor environmental elements pertinent to its objectives. Our research introduces a neurosymbolic modular architecture for reactive robotics. Our system combines a neural component performing object recognition over the environment and image processing techniques such as optical flow, with symbolic representation and reasoning. The reasoning system is grounded in the embodied cognition paradigm, via integrating image schematic knowledge in an ontological structure. The ontology is operatively used to create queries for the perception system, decide on actions, and infer entities’ capabilities derived from perceptual data. The combination of reasoning and image processing allows the agent to focus its perception for normal operation as well as discover new concepts for parts of objects involved in particular interactions. The discovered concepts allow the robot to autonomously acquire training data and adjust its subsymbolic perception to recognize the parts, as well as making planning for more complex tasks feasible by focusing search on those relevant object parts. We demonstrate our approach in a simulated world, in which an agent learns to recognize parts of objects involved in support relations. While the agent has no concept of handle initially, by observing examples of supported objects hanging from a hook it learns to recognize the parts involved in establishing support and becomes able to plan the establishment/destruction of the support relation. This underscores the agent’s capability to expand its knowledge through observation in a systematic way, and illustrates the potential of combining deep reasoning with reactive robotics in dynamic settings.