Mutual intention recognition for human-robot cooperation

• Applicant: Professor Dr. Dominik Henrich
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: since 2023
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 513159450

In traditional automation systems, robots are used to repeatedly perform the same task for a long period of time. Fences often ensure the safety of human workers by preventing them from entering the robot workspace. Recent advances enable safe human-robot coexistence without these physical barriers. With humans and robots being able to move safely within the shared workspace, it is possible to integrate human agents into the production process. Humans and robots can work on a common goal using their individual skills. With this symbiotic combination of intelligent robotics and the cognitive skills that are unique to humans, hybrid and flexible cooperating systems can be designed that are useful for many applications, such as small-series production, service, workshops, laboratories and households. The goal of the research project is the development and evaluation of mutual intention recognition in the context of a concept for human-robot cooperation. In this context, humans and robots are to act as equal partners that can freely and dynamically choose their actions. The project assumes that the partners can communicate their intentions non-verbally. Ideally, the partners can coordinate dynamically during execution. A newly developed task representation that can encode alternative approaches and observation uncertainties shall form the foundation for representing different plans. Communication is subdivided into communicating a message and understanding it. For the robot, the concepts of intention recognition and legibility shall achieve the goal. Hidden Markov models, motion prediction and human-aware motion planning are used as approaches. Central questions to be answered are how these concepts can predict the current action and fairly long-term plans and how team fluency benefits from the interaction of the concepts.

• DFG Programme: Research Grants