Thomas H. Weisswange

Moritz C. Buehler, Jürgen Adamy, Thomas H. Weisswange

When cooperating with a human, a robot should not only care about its environment and task but also develop an understanding of the partner's reasoning. To support its human partner in complex tasks, the robot can share information that it knows. However simply communicating everything will annoy and distract humans since they might already be aware of and not all information is relevant in the current situation. The questions when and what type of information the human needs, are addressed through the concept of Theory of Mind based Communication which selects information sharing actions based on evaluation of relevance and an estimation of human beliefs. We integrate this into a communication assistant to support humans in a cooperative setting and evaluate performance benefits. We designed a human robot Sushi making task that is challenging for the human and generates different situations where humans are unaware and communication could be beneficial. We evaluate the influence of the human centric communication concept on performance with a user study. Compared to the condition without information exchange, assisted participants can recover from unawareness much earlier. The approach respects the costs of communication and balances interruptions better than other approaches. By providing information adapted to specific situations, the robot does not instruct but enable the human to make good decision.

Chao Wang, Thomas H. Weisswange, Matti Krueger et al.

To maximize safety and driving comfort, autonomous driving systems can benefit from implementing foresighted action choices that take different potential scenario developments into account. While artificial scene prediction methods are making fast progress, an attentive human driver may still be able to identify relevant contextual features which are not adequately considered by the system or for which the human driver may have a lack of trust into the system's capabilities to treat them appropriately. We implement an approach that lets a human driver quickly and intuitively supplement scene predictions to an autonomous driving system by gaze. We illustrate the feasibility of this approach in an existing autonomous driving system running a variety of scenarios in a simulator. Furthermore, a Graphical User Interface (GUI) was designed and integrated to enhance the trust and explainability of the system. The utilization of such cooperatively augmented scenario predictions has the potential to improve a system's foresighted driving abilities and make autonomous driving more trustable, comfortable and personalized.