The implementation and design of cognitive Robot-Systems is a challenging process. Scientists and Enginerings must overview the physics of robots & their environments, sensors & sensor data, perception, probabilistic state estimation, knowledge representation and reasoning, robot learning, task and motion control, and cognitive architectures. It takes just as much effort to consider these issues when designing robots as it does to teach the entirety of robotics. How can we establish and foster the capabilities to understand the interrelation of constituent system parts and how systems work over time and within the context of larger systems (System thinking); the decomposition, pattern recognition, abstraction, and algorithm design (Computational thinking); the intrinsic motivation and willingness to solve challenges of robotics (Motivation); strategies, processes and abstraction (Mathematical thinking); the ability to make reasoned decisions (Decision making); and the awareness on how technology impacts (Sustainability)?

In this workshop, we address this very challenge by systematizing a best practice from experienced teachers. How is it possible to integrate the needs of all stakeholders? What are the perspectives of Students, Educators & Researchers, as well as state of the art in cognitive robotics? All of these considerations should also include the demands of society on the subject of cognitive robotics.

Invited Speakers


Content of the workshop

One (i) objective is to discuss the following proposed competences of cognitive roboticists
  • System thinking - understanding of interrelation of system's constituent parts and how systems work over time and within the context of larger systems. To achieve this, students must be enabled to develop an understanding of a system's structure, the dynamic behavior of a system, system at a different scale, and reduce the complexity of a system (cf [Ross D. Arnold \& Jon Wade, 2015]). Applied to robotics, a roboticist needs to develop an awareness of the purpose of a robotic system as well as an understanding of the characteristics of a robotic system. In short: A roboticist must overview the hardware system, the software components, as well as, the implications of a developing environment.
  • Computational thinking - Decomposition, pattern recognition, abstraction, and algorithmic design.
    Teaching cognitve robotics includes the capability to decompose a comples problem or task into easy to solvable or understandable subtasks. TODO: Cognitive Robotics special
    Following the decomposition, subtasks are solved by identifying patterns. These solutions are based on experience and known problem solving processes.
    Understanding these patterns allows it to foucs on the most relevant aspects of the problem. Unimportant aspects of the problem are omitted. The focus is on the relevant details. The initial problem is greatly simplified.
    Finally the understanding of tasks, subtasks, patterns, and abstractions can be defined in rules or algorithms.
  • Mathematical thinking - problem solving and strategies, modeling of processes and objects, abstraction of objects and processes (Kodde-Buitenhuis, 2015)
  • Decision making - the ability to make reasoned decisions
  • Technology impact awareness - the ability to understand and reflect the effect of the developed system (Sustainability competence)
  • Active learning - the intrinsic motivation and willingness to solve challenges of robotics
Since the list of professional and volitional competences has been created normatively, it is necessary to check here whether missing competences should be added or the mentioned competences should be described differently. In addition to the goals of taking a closer look at the competencies of cognitive robotics, it is also essential to discuss approaches that consider the content's complexity. The (ii) objective is to understand how cognitive robotics can be taught. And in the end how (iii) how to create a cognitive robotics competence aware curriculum. In higher education and vocational training there are a number of didactic approaches that have been established in the recent past. One of these approaches is Constructive Alignment (Biggs 1996), which includes teaching and learning methods as well as assessment methods and intended learning goals. In brief, Biggs and Tang 2011 summarize the goal of student-centered teaching as follows: Learners must be given the opportunity to achieve the intended learning goals through the teaching and learning content provided. This easily comprehensible demand on teaching and learning processes ultimately results in skills or competences that students can incorporate into their learning personas and apply in later contexts. The workshop will focus on an exemplary methodology that is being used in the preparation of the teaching of a new master's program in cognitive robotics at the University of Bremen, Germany. In this course, research-oriented teaching is used alongside the ideas of Constructive Alignment, in which students can approach topics and tools of research in the field of cognitive robotics in an interactive and immersive teaching and learning environment that is currently under construction. Part of this conception of teaching is the AVIVA method (Städeli et. al. 2010), which originated in Switzerland. This method focuses on the active design of learning phases by the learners, in which phases of learning are directed to the respective content and prior knowledge of the learners. After this connection to previous knowledge, new content is introduced and later applied and evaluated by the students. This concept will be discussed with the participants and invited guests through further examples, e.g. the introduction of a robotics major at Michigan University or the robotics course at CMU Africa. Each session of the workshop is adressing the challenge of cognitive robotics and invites the participants to discuss based on their experience. To enable the participants to approach the respective aspect, invited speakers will give a keynote speech and open the topic’s discussion. The results of the discussion will be recorded on an online whiteboard. The presentations are short-form presentations lasting only a few minutes. With this workshop we address (Early Career) Researchers, Educators, and policy makers in the field of research,governance as well as education. Thinking about teaching robotics in general and cognitive robotics in particular and how to enable roboticists to develop capabilites to solve robotic problems is challenging. The “Teaching & Training students for cognitive robotics” workshop will add the teaching and learning perspective to IROS 2023.


08:30 – 09:00Opening Session – Jörn Syrbe 
09:00 – 10:00Session 2: Research-oriented Teaching  – Michael Beetz 
10:00 – 11:00Coffee break 
11:00 – 12:30Session 3 
12:30 – 13:30Lunch 
13:30 – 14:00Session 4 
14:00 – 15:00Session 5 
15:00 – 16:00Coffee break 
16:00 – 17:30Session 6 


Target audience

• Educators, Policy makers, Researchers

Financial Support

The workshop will be supported by the Collaborative Research Center "Everyday Activity Science and Engineering (EASE)," funded by the German Research Foundation (DFG) at the University of Bremen. The workshop supported by the infrastructure project "Integrated e-Learning for Cognitive Robotics (IntEL4CoRo)", funded by the German Federal Ministry of Education and Research (BMBF).

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