T1: Robots as Significant Others – Understanding Social Agency of Robots
How does a machine such as a robot achieve social agency? I.e. how do humans construct a “significant other” in their interaction with a robot? How will increased social interaction with robots impact social order in interaction with this technology? With the recent advancements in artificial intelligence and social robotics in the design of technologies that engage in social interaction, these questions become more and more important.
T2: Trusting Robots need Joint Attention
Communication relies on mutual understanding of intentions. One of the key ability of humans is to obtain intentional information from motion and gesture. This capability to exploit attention for rapidly obtaining the relevant task (intention) information is reasonably understood. For example, when communicating the gaze is essential to create joint attention and understanding. Furthermore, humans can deploy the learned abstracted model and generalise to a new setting with new people and objects. In this way we learn how what others intend to do, a large part of mutual trust. This thesis will study how these mechanisms can be implemented on robots.
T3: Body Language in Human-Robot Interaction
Nonverbal communication based on natural body language is a powerful means of conveying and perceiving human emotions. In human-robot interaction, coherence between different communication channels such as voice, gaze and body gestures is an important requirement for mutual understanding and trustful collaboration. On the one hand, robots with specific gestures and motion patterns that embody human feelings – such as emotions, moods and attitudes – increase trust in human-robot interaction. On the other hand, robotic agents that are able to interpret intentions and emotions of humans in their surrounding can provide improved situation-aware reactions. The task of capturing and analysing human posture and motion patterns by computer vision methods for subsequent interpretation in terms of emotional cues provides challenging research questions that have been addressed in only few studies so far.
T4: Scene Understanding for Knowing about Objects and their Use
Humans will have much less control over a robot than over a handheld. Although robots get safer and more able everyday, it seems rather terrifying to hand over control of daily aspects to a robot. For example, a robot keeps track of all items and belongings in the home where it is placed. Will humans accept that robots do not forget? Alternatively, will humans forget since they can ask the robot any time, as it happened with telephone numbers?
T5: Safe Human Robot Collaboration
In recent years, the barriers between robots and humans have been coming down and a new generation of robots is designed to closely collaborate with humans. Typical applications of collaborative manipulation in industry involve assembly, grasping, material shaping, and load sharing tasks. The ultimate goal is to provide humans with a highly flexible tool in the form of a cognitive collaborative robot. In this context, a safe human-machine interaction is a crucial prerequisite for this technology. Thus, there is a need to safely coordinate and orchestrate the motion of the robots involved based on an understanding and anticipation of the human’s intention in the shared workspace and the tasks to be performed.
T6: Skepticism to Overconfidence – Trust in Autonomous Robot Decision Making and Operation in Human-Robot Collaboration
Trust is a necessity in efficient and effective collaboration between humans. Lack of trust could result in unnecessary and therefore inefficient control steps, rework or caution. On the other hand exaggerated trust can lead to poor and therefore ineffective results due to lack of control or group think. The industrial use of robots at the moment is – with some exception – basically restricted to the clear separation of human and robot tasks, where the later are strictly programmed and conducted in an isolated work space. However, with the increasing level of automation and digitalization collaboration scenarios between humans and autonomous robots gain importance and are at the edge of realization.
T7: Division of Labour in Human-Robot-Interaction in Hybrid Manufacturing Settings
Technological advances, recent price drops and positive experiences with industrial implementation of collaborative robotics have led to a widespread interest in lightweight robot (arm)s in manufacturing processes. Whereas some processes tend to be completely substituted by advances in robotics (loading, unloading, simple logistics processes), manufacturing trends such as challenging lead time requirements, demand volatility and decreasing lot sizes drive the need for true collaboration of workers and robots (work at the same time within the same workspace at the same work piece). Current research and industrial practise is largely focussing on the implementation of first demonstrators and (physical) safety issues. As the state-of-the-art and the experience in safe human-robot collaboration advances, future application scenarios will focus on the division of labour and the optimal way of harnessing the specific advantages of human- robot interaction.
Topic 8: Confidence in Decisions and Actions – Space as a Dimension of Trust in Social Robots
At present, humanoid robots are penetrating into areas of everyday life and thus into social spaces also. Robotic systems that are supposed to operate in the human environment are characterized by the automatic execution of decisions and actions by means of behaviours in social space. This topic examines living space as a dimension of trust in life with social robots within Ambient Assisted Living environments (AAL) of various technological facilities. Confidence in the decisions and actions of technical assistance systems is essential for their acceptance. Which spatial structures, atmospheres and technological configurations support or make it difficult to build and maintain trust in social robots? What can designers of humanoids robots as well as architects do to allow for trust to unfold, in terms of hardware, software, shape, form and behaviour?
T9: The Interrelation of Safety Assessment and Trust in Uncertain and Dynamic Human-Robot Cooperation
Industrial robots, designed to increase productivity for automated tasks, have a long history in the manufacturing industry. One of the major challenges in Industry 4.0 scenarios is to enable a more powerful human-robot relationship, which however needs to ensure safe and reliable cooperation between robots and operators. Above all in short-term interactions such as turn taking (an operator performs some work, then the robot continues, then the human tasks over again etc.), it is still an open issue how the ideal trusting and safe relationship between the human and the intelligent (autonomous) system should look like.
Topic 10: Robot Anthropomorphism, Trust, and Machine Transparency
Humanoid robots such as Pepper or Nao are playing with a high degree of anthropomorphising their appearance and behaviour. However, does a design focussing on human-likeness come at a cost? Since appearance and behaviour design of embodied and non-embodied agents can have an impact on the human-technology relationship, it is important to study their effect. On the one hand, first studies have indicated that highly human-like robots are perceived as less trustworthy and empathetic than more machine-like robots. On the other hand, faulty behaviour of a more machine-like robots reduces trustworthiness. There seems to be an interaction triangle between anthropomorphism (in appearance and behaviour), machine transparency (end user understanding of actual machine capacities), and trustworthiness that needs further exploration.