This report introduces our UniTeam agent - an improved baseline for the "HomeRobot: Open Vocabulary Mobile Manipulation" challenge. The challenge poses problems of navigation in unfamiliar environments, manipulation of novel objects, and recognition of open-vocabulary object classes. This challenge aims to facilitate cross-cutting research in embodied AI using recent advances in machine learning, computer vision, natural language, and robotics. In this work, we conducted an exhaustive evaluation of the provided baseline agent; identified deficiencies in perception, navigation, and manipulation skills; and improved the baseline agent's performance. Notably, enhancements were made in perception - minimizing misclassifications; navigation - preventing infinite loop commitments; picking - addressing failures due to changing object visibility; and placing - ensuring accurate positioning for successful object placement.

How can artificial intelligence (AI) draw on principles from nature to solve complex problems? When it comes to recognizing patterns in large amounts of data, AI is faster and more capable than humans. However, it has difficulties when it has to make connections or deal with uncertainties and fuzziness. Through evolution, development, and learning, nature has developed much more practical problem-solving solutions. Yaochu Jin, the Alexander von Humboldt Professor of Artificial Intelligence at Bielefeld University since the autumn, is looking at how such principles can be transferred to AI. The Humboldt Professor will be continuing his previous research on nature-inspired artificial intelligence at Bielefeld University and looking for applications of nature-inspired and self-organized AI. 'My goal is to understand and borrow successful mechanisms from nature and transfer them into artificial intelligence for problem-solving,' says Jin.

Yaochu Jin is doing research on nature-inspired intelligent technological systems that organize themselves in a changing environment. How can artificial intelligence (AI) draw on principles from nature to solve complex problems? When it comes to recognizing patterns in large amounts of data, AI is faster and more capable than humans. However, it has difficulties when it has to make connections or deal with uncertainties and fuzziness. Through evolution, development, and learning, nature has developed much more practical problem-solving solutions. Yaochu Jin, the Alexander von Humboldt Professor of Artificial Intelligence at Bielefeld University since the autumn, is looking at how such principles can be transferred to AI.

Sifting through job applications, analyzing X-ray images, suggesting a new track list--interaction between humans and machines has become an integral part of modern life. The basis for these artificial intelligence (AI) processes is algorithmic decision-making. However, as these are generally difficult to understand, they often prove less useful than anticipated. Researchers at Paderborn and Bielefeld University are hoping to change this, and are discussing how the explainability of artificial intelligence can be improved and adapted to the needs of human users. Their work has recently been published in the respected journal IEEE Transactions on Cognitive and Developmental Systems.

Autonomous vehicles and devices for intelligent homes are becoming increasingly complex. A new system based on machine learning is being designed to make the soft- and hardware used for these applications more robust, powerful, and energy-efficient. The new project VEDLIoT is being funded by the European Commission, with approximately eight million Euro over the course of three years. The project is coordinated by Bielefeld University's CoR-Lab. In an intelligent home - a "Smarthome" - residents have devices at their fingertips that are designed to make lives easier: imagine a refrigerator that re-orders food when it is running low, and can, at the same time, communicate with the oven.

Voice assistants, smart homes, or industrial 4.0 systems: artificial intelligence (AI) is increasingly automating processes in a wide variety of living and working environments. However, AI systems often prove to be not particularly competent because they lack either background or contextual knowledge, are unable to assess the scope and implications of assumptions and decisions, and cannot explain their actions. In the Joint Artificial Intelligence Institute (JAII), the two universities at Bielefeld and Paderborn are combining their research competencies in this field of research. The universities jointly founded the institute on July 14, 2020. In the JAII, future research will address the fundamentals of AI systems designed to focus on people.

The challenge to develop an integrated perspective of embodiment in communication has been taken up by an international research group hosted by Bielefeld University's Center for Interdisciplinary Research (ZiF--Zentrum für interdisziplinäre Forschung) from October, 2005 through September, 2006. An international conference was held there on 12-15 January, 2005 to define a research agenda that will explicitly address embodied communication in humans and machines. Psychologist Bennett Bertenthal (University of Chicago, USA), and psycholinguists Sotaro Kita (University of Bristol, UK) and Susan Duncan (University of Chicago, USA) pointed out how gestures form an intimate connection with human speech. Moreover, gestures are responsive to the gestures of others, coupling speakers and listeners in a close "social loop" in which a realtime processing of bodily signals seems at least as important as semantic analysis of words and sentences.

Researchers at Bielefeld University have developed a grasping learning system based on robotic hands. It is capable of autonomously familiarizing itself with unknown objects. The system called "Famula" works without knowing the specific characteristics of objects such as pieces of fruit or tools in advance. "Our system learns by trying out and exploring by itself – in the same way that babies approach new objects", says Prof. Dr. Helge Ritter. The knowledge generated in the project could contribute towards enabling future service robots to become autonomously acquainted in new household environments. The grasping learning system was developed as part of the "Famula" large-scale project at Bielefeld University's Cluster of Excellence Cognitive Interaction Technology (CITEC).

Developed by the Bielefeld AI group, Max can imitate human gestures and exhibit humanlike synthetic speech and coverbal gesture while constructing an airplane from a construction kit in cooperation with a human partner. In an invitational bodily communication could be captured communication. Does a body need flesh highly acclaimed speakers from various Italy) who presented ongoing research and blood?, linguist Jens Allwood disciplines presented their perspectives on mode-specific lexicons, (University of Göteborg, Sweden) pertaining to conceptual issues such as "gestionaries," "gazeionaries" asked, or might wire and metal be of embodiment; the phylo-and and "touchionaries," as an equivalent sufficient, or a simulation in virtual ontogenesis of communication; bodily of dictionaries in spoken language. When apes can learn to control gestures; understanding and communicating How these ideas could be integrated a robot arm through an electrode intentions, emotions, in human-machine interaction was implanted in their brains, philosopher and symbols; and the role of bodily addressed by computer scientist Joëlle Proust (Institut Jean-action in language and speech. The Catherine Pelachaud (Université de Nicod, Paris, France) added, then talks were centered around the two Paris 8, Montreuil, France).