TIAGo robot helping in kitchen in the Cognitive Service Robotics Apartment lab

Researchers around the world can access our Virtual Research and Training Building.

© Bielefeld University/Patrick Pollmeier

Breaking new ground in AI-enabled robotics

Our Vision: The Joint Research Center on Cooperative and Cognition-enabled AI (CoAI JRC) is Germany’s leading center for interdisciplinary research and innovation on cooperative and cognition-enabled artificial intelligence that features human-centered, embodied, and real-world agency in the sense that it is capable of acting together with humans in a meaningful and goal-directed manner.

The central hypothesis of CoAI is that AI systems need to be equipped with powerful cognitive, reasoning, communicative and interaction abilities to successfully act in the physical and social world. They have to understand and reason about their own actions and those of others as a basis to provide contextualized support for the wellbeing of humans and society at large.

Robot arm helping a human with an injured hand

AI that works cooperatively in human physical and social environments

Our Mission: The researchers of CoAI JRC join forces to break new ground in the communication and interaction of humans and robots by developing AI systems that have a deep and actionable understanding of how to perform everyday joint tasks in natural human environments. The AI systems targeted by CoAI are able to successfully act in the physical and social world by adapting and learning new skills through cooperating and communicating with their human partners to eventually become competent and trustworthy partners.

Discover our virtual research and training building

Openness is the most efficient and sustainable approach to successful innovation in our current fast-moving AI research environment. This is why the CoAI Center aims to provide external researchers with convenient access to a Virtual Research and Training Building (ViB), which will enable scientists from anywhere in the world to work as if the are physically present in our labs. In the ViB, members of the international CoAI research community have access to AI-ready digital twin robots, research laboratories, and environments to conduct research in AI-enabled robotics, human-robot interaction, education science and developmental robotics. We also support a variety of open data and open software tools, such as the cognitive robot architecture CRAM.

Operating a digital twin PR2 in a virtual kitchen lab
© University of Bremen/Patrick Pollmeier

News & Events

Katharina Rohlfing and Britta Wrede (left to right)
© Susanne Freitag / Michael Adamski

Building trust in science for UNESCO World Science Day

News
26.01.24

“FAME” Research Initiative by the CoAI JRC’s Michael Beetz will Enable Robots to Visualize Future Actions

An important challenge for cooperative and cognition-enabled robotics is to develop robots that have the ability to consider different possible courses of action and their likely outcomes before selecting one. This is especially important for robots that are engaged in joint tasks with human partners in which the sub-tasks and individual steps have not yet […]
News
17.01.24

CoAI Virtual Building Featured in Up2Date

The CoAI Virtual Research and Training Building is featured in the latest issue of up2date, the online magazine of the University of Bremen: “Tall windows, a wide, bright hallway, a mirrored elevator: the virtual research building on cognitive-based AI can be entered like a location in a computer game which is waiting to be explored. […]
15.01.2024, 16:00 - 18:00

Talk by Johanna Seibt on the Problem of Artificial “Social Others”

Title: The Problem of Artificial “Social Others”: How Much Sociality Do We Need for Hybrid Intelligence? Abstract: Co-working, co-creating, cooperation, collaboration—social interactions among humans take many different forms even when the single actions involved are functionally similar: What we take ourselves to be doing together, depends on how we do it together. This raises a […]

The CoAI JRC Is Part of a Thriving AI Research Ecosystem

The CoAI JRC is embedded in a network of independent projects, research initiatives, and partners in industry and the public sector that collectively form a thriving ecosystem of research in AI and robotics.

Comparing generative and extractive approaches to information extraction from abstracts describing randomized clinical trials (2024)

Authors: Christian Witte, David M. Schmidt, Philipp Cimiano
Published at: Journal of Biomedical Semantics (Volume: 15)

Lactation support in neonatal intensive care units in Germany from the mothers’ perspective – a mixed-method study of the current status and needs (2024)

Authors: Isabella Schwab, Ricarda Wullenkord, Friederike Eyssel, Till Dresbach, Nadine Scholten
Published at: BMC Pregnancy and Childbirth (Volume: 24)

Feature-based analyses of concept drift (2024)

Authors: Fabian Hinder, Valerie Vaquet, Barbara Hammer
Published at: Neurocomputing (Volume: 600)

CecApprox: Enabling Automated Combinational Equivalence Checking for Approximate Circuits (2024)

Authors: Chandan Kumar Jha, Muhammad Hassan, Rolf Drechsler
Published at: IEEE Transactions on Circuits and Systems I: Regular Papers (Volume: 71)

Error bounds for kernel-based approximations of the Koopman operator (2024)

Authors: Friedrich M. Philipp, Manuel Schaller, Karl Worthmann, Sebastian Peitz, Feliks Nüske
Published at: Applied and Computational Harmonic Analysis (Volume: 71)

Prior experience of variability influences generalisation of unspecified categories (2024)

Authors: Ann-Katrin Hosch, Philipp Wirtz, Bettina von Helversen
Published at: Quarterly Journal of Experimental Psychology (Volume: 77)

Exploring the Potential of Decision Diagrams for Efficient In-Memory Design Verification (2024)

Authors: Khushboo Qayyum, Abhoy Kole, Kamalika Datta, Muhammad Hassan, Rolf Drechsler
Published at: Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Open Source Dual-Purpose Acrobot and Pendubot Platform: Benchmarking Control Algorithms for Underactuated Robotics (2024)

Authors: Felix Wiebe, Shivesh Kumar, Lasse J. Shala, Shubham Vyas, Mahdi Javadi, Frank Kirchner
Published at: IEEE Robotics and Automation Magazine (Volume: 31)

Conditional feature importance for mixed data (2024)

Authors: Kristin Blesch, David S. Watson, Marvin N. Wright
Published at: AStA Advances in Statistical Analysis (Volume: 108)

Universal Knowledge Graph Embeddings (2024)

Authors: N'Dah Jean Kouagou, Caglar Demir, Hamada M. Zahera, Adrian Wilke, Stefan Heindorf, Jiayi Li, Axel-Cyrille Ngonga Ngomo
Published at: WWW 2024 Companion - Companion Proceedings of the ACM Web Conference