Real-world robotic applications, from autonomous exploration to assistive technologies, require adaptive, interpretable, and data-efficient learning paradigms. While deep learning architectures and foundation models have driven significant advances in diverse robotic applications, they remain limited in their ability to operate efficiently and reliably in unknown and dynamic environments. In this position paper, we critically assess these limitations and introduce a conceptual framework for combining data-driven learning with deliberate, structured reasoning. Specifically, we propose leveraging differentiable physics for efficient world modeling, Bayesian inference for uncertainty-aware decision-making, and meta-learning for rapid adaptation to new tasks. By embedding physical symbolic reasoning within neural models, robots could generalize beyond their training data, reason about novel situations, and continuously expand their knowledge. We argue that such hybrid neuro-symbolic architectures are essential for the next generation of autonomous systems, and to this end, we provide a research roadmap to guide and accelerate their development.

Conditional information is an integral part of representation and inference processes of causal relationships, temporal events, and even the deliberation about impossible scenarios of cognitive agents. For formalizing these inferences, a proper formal representation is needed. Psychological studies indicate that classical, monotonic logic is not the approriate model for capturing human reasoning: There are cases where the participants systematically deviate from classically valid answers, while in other cases they even endorse logically invalid ones. Many analyses covered the independent analysis of individual inference rules applied by human reasoners. In this paper we define inference patterns as a formalization of the joint usage or avoidance of these rules. Considering patterns instead of single inferences opens the way for categorizing inference studies with regard to their qualitative results. We apply plausibility relations which provide basic formal models for many theories of conditionals, nonmonotonic reasoning, and belief revision to asses the rationality of the patterns and thus the individual inferences drawn in the study. By this replacement of classical logic with formalisms most suitable for conditionals, we shift the basis of judging rationality from compatibility with classical entailment to consistency in a logic of conditionals. Using inductive reasoning on the plausibility relations we reverse engineer conditional knowledge bases as explanatory model for and formalization of the background knowledge of the participants. In this way the conditional knowledge bases derived from the inference patterns provide an explanation for the outcome of the study that generated the inference pattern.

Acceptance, trust and a successful deployment of robots in human's everyday life depends both on technical implementation and on psychological aspects. The current work identifies essential features and characteristics that have the potential to increase the acceptance of robots, reduce prejudices and improve the development of appropriate and suitable robots with target-group specific features and characteristics. We present the first part of a major research project that aims to develop a valid and reliable toolkit for an encompassing measurement of human's attitude towards robots. By means of the Semantic Differential Scale a comparison of human's attitude towards robots in general and human's expectation of an ideal, personal everyday life robot has been performed. Results reveal major differences between these two concepts and the demand of robots adapted to human's requirements.

Perfect memory, strong reasoning abilities and flawless performance are typical cognitive traits associated with robots. In contrast, forgetting and erroneous reasoning are typical cognitive patterns of humans. This discrepancy may fundamentally affect the way how robots and humans interact and collaborate together and is today still little explored. In this paper, we investigate the effect of differences between erroneous and perfect robots in a competitive scenario in which humans and robots solve reasoning tasks and memorize numbers. Participants are randomly assigned to one of two groups: in the first group they interact with a perfect, flawless robot, while in the second, they interact with a human-like robot with occasional errors and imperfect memorizing abilities. Participants rate attitude, sympathy, and attributes of the robot in a questionnaire and we measure their task performance. The results show that the erroneous robot triggered more positive emotions but lead to a lower human performance than the perfect one. Effects of both conditions on the group of students with and without technical background are reported.

Humans use very sophisticated ways of bodily emotion expression combining facial expressions, sound, gestures and full body posture. Like others, we want to apply these aspects of human communication to ease the interaction between robots and users. In doing so we believe there is a need to consider what abstraction of human social communicative behaviors is appropriate for robots. The study reported in this paper is a pilot study to not offer simulated emotion but to offer an abstracted robot version of emotion expressions and an evaluation to what extent users interpret these robot expressions as the intended emotional states. To this end, we present the mobile, mildly humanized robot Daryl, for which we created six motion sequences that combine human-like, animal-like, and robot-specific social cues. The results of a user study (N=29) show that despite the absence of facial expressions and articulated extremities, subjects' interpretation of Daryl's emotional states were congruent with the abstracted emotion display. These results demonstrate that abstract displays of emotion that combine human-like, animal-like, and robot-specific modalities could in fact be an alternative to complex facial expressions and will feed into ongoing work identifying robot-specific social cues.

Empirical findings indicate that humans draw infer- ences about spatial arrangements by constructing and manipulating mental models which are internal representations of objects and relations in spatial working memory. Central to the Mental Model Theory (MMT), is the assumption that the human reasoning process can be divided into three phases: (i) Mental model construction, (ii) model inspection, and (iii) model validation. The MMT can be formalized with respect to a computational model, connecting the reasoning process to operations on mental model representations. In this respect a computational model has been implemented in the cognitive architecture ACT-R capable of explaining human reasoning difficulty by the number of model operations. The presented ACT-R model allows simulation of psychological findings about spatial reasoning problems from a previous study that investigated conventional behavioral data such as response times and error rates in the context of certain mental model construction principles.

In this article, we introduce a complexity measure for matrix tasks which occur in IQ-tests with respect to the kind of functions necessary to solve such tasks. The aim is to capture human reasoning difficulty. We implemented a program which is able to solve matrix tasks and to evaluate their complexity by our measure. The results of the evaluation are compared with the empirical difficulty ranking from Cattell's Culture Fair Test.