Non-verbal behaviors, such as body movements, play a crucial role in enhancing a robot’s speech to elevate engagement in human-robot dialogue. Many existing approach based on rules offered natural and engaging motions aligned with the robot’s utterances but required significant resources to maintain. Recent methods leveraging large language models (LLMs) offer a promising alternative to reduce these costs. However, there is a trade-off between flexibility and safety when determining whether the language model should generate motions based on joint angle parameters or action primitives. In this study, we evaluated two LLM-based motion control models: one for motion generation based on joint angle parameters (LLM-GJA) and the other for motion generation based on primitive actions (LLM-GPA). Our human evaluations indicated that directly generating joint angles outperformed generating action primitives in naturalness, timing consistency, and overall engagement, even achieving performance comparable to rule-based systems. This work highlights the potential of LLMs in generating expressive and contextually appropriate robot motions at the joint angle level.

The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, uncertainty is hardly taken into account. Changes in the definition of a model, for instance, could have dramatic effects on the outcome of simulations. Therefore, neither reduced models nor initial conclusions could be 100% relied upon. In this research, Interval Constraint Solving Techniques (ICST) are employed to handle and quantify uncertainty. The goal is to identify key features of a given dynamical phenomenon in order to be able to propagate the characteristics of the model forward and predict its future behavior to obtain 100% guaranteed results. This is specifically important in applications, as a reliable understanding of a developing situation could allow for preventative or palliative measures before a situation aggravates.

In Multi-Criteria Decision Making (MCDM), decisions are based on several criteria that are usually conflicting and non-homogenously satisfied. Non-additive (fuzzy) measures along with the Choquet integral can model and aggregate the levels of satisfaction of these criteria by considering their relationships. However, in practice, it is difficult to identify such fuzzy measures. An automated process is necessary and can be used when sample data is available. Several optimization approaches have been proposed to extract fuzzy measures from sample data; for example, genetic algorithms, gradient descent algorithms, and the Bees algorithm. In this article, instead of using the search space as the primary focus of our research, we propose an algorithm that speculates on the value of the objective function before actually arriving to it. In addition, contrary to previous approaches to extracting fuzzy measures, our algorithm guarantees the solution to be global. Our experimental results show that our algorithm improves the performance of previous approaches.