There is a strong need for robots capable of efficiently performing tasks in workspaces shared with humans. In fact, recent advances in robot safety have enabled the development of robots capable of sharing their workspace with humans without endangering them. However, safety has come at the expense of efficiency, and as a result, such robots have seen limited success: beyond safety, human trust and joint productivity remain unresolved challenges.
The objectives of this chair are to equip robots with the decision-making capacity to navigate shared environments not only safely but also efficiently through nonverbal cooperation and, ideally, without disrupting the social environment. We focus specifically on logistics scenarios, where robots must possess significant speed and inertia to be useful, making these issues crucial and significantly more demanding than in the existing literature, where collaborative robots typically have reduced speed and inertia.
Our strategy is to develop a layered approach, addressing motion safety with traditional symbolic AI (automated planning and constraint-based reasoning), nonverbal cooperation with Reinforcement Learning (RL), and social integration with Imitation Learning (IL).
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Published on October 13, 2026 Updated on October 13, 2026
Rudolf Lioutikov (Karlsruhe Institute of Technology)
Research Topics
Mobile Robotics,
Social Robotics,
Model Predictive Control,
Reinforcement Learning,
Inverse Reinforcement Learning,
Goal-Conditioned Imitation Learning
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