Modeling Human-Robot-Interaction based on generic Interaction Patterns

Peltason J. Modeling Human-Robot-Interaction based on generic Interaction Patterns. Bielefeld: Bielefeld University; 2014

Engagement-based Multi-party Dialog with a Humanoid Robot

When a robot is situated in an environment containing multiple possible interaction partners, it has to make decisions about when to engage specific users and how to detect and react appropriately to actions of the users that might signal the intention to interact. In this demonstration we present the integration of an engagement model in an existing dialog system based on interaction patterns. As a sample scenario, this enables the humanoid robot Nao to play a quiz game with multiple participants

The Curious Robot as a Case-Study for Comparing Dialog Systems

Peltason J, Wrede B. The Curious Robot as a Case-Study for Comparing Dialog Systems. AI Magazine. 2011;32(4):85-99.Modeling interaction with robots raises new and different challenges for dialog modeling than traditional dialog modeling with less embodied machines. We present four case studies of implementing a typical human-robot interaction scenario with different state-of-the-art dialog frameworks in order to identify challenges and pitfalls specific to HRI and potential solutions. The results are discussed with a special focus on the interplay between dialog and task modeling on robots

Modeling Human-Robot Interaction Based on Generic Interaction Patterns

Peltason J, Wrede B. Modeling Human-Robot Interaction Based on Generic Interaction Patterns. In: AAAI Fall Symposium: Dialog with Robots. Arlington, VA, USA: AAAI Press; 2010.While human-robot interaction is typically limited to restrictive command-control style, traditional dialog modeling approaches are not directly applicable to robotics due to the lack of real-world integration. We present an approach that combines insights from dialog modeling with software-engineering demands that arise in robotics system research to provide a generalizable framework that can easily be applied to new scenarios. This goal is achieved by defining interaction patterns that combine abstract task states (such as task accepted or failed) with robot dialog acts (such as assertion or apology). An evaluation of the usability for robotic experts and novices showed that both groups were able to program 3 out of 5 dialog fragments in one hour while showing a steep learning curve. We argue that the proposed approach allows for less restricted and more informative human-robot interactions

Pamini: A framework for assembling mixed-initiative human-robot interaction from generic interaction patterns

Peltason J, Wrede B. Pamini: A framework for assembling mixed-initiative human-robot interaction from generic interaction patterns. In: SIGDIAL 2010 Conference. Association for Computational Linguistics; 2010.Dialog modeling in robotics suffers from lack of generalizability, due to the fact that the dialog is heavily influenced by the tasks the robot is able to perform. We introduce interleaving interaction patterns together with a general protocol for task communication which enables us to systematically specify the relationship between dialog structure and task structure. We argue that this approach meets the requirements of advanced dialog modeling on robots and at the same time exhibits a better scalability than existing concepts

The Curious Robot learns grasping in multi-modal interaction

Lütkebohle I, Peltason J, Haschke R, Wrede B, Wachsmuth S. The Curious Robot learns grasping in multi-modal interaction. In: ICRA Workshop Interactive Communication for Autonomous Intelligent Robots. 2010

Talking with Robots about Objects: A system-level evaluation in HRI

Peltason J, Riether N, Wrede B, Lütkebohle I. Talking with Robots about Objects: A system-level evaluation in HRI. In: 7th ACM/IEEE Conference on Human-Robot-Interaction. 2012.We present the design process, realization and evaluation of a robot system for interactive object learning. The system-oriented evaluation, in particular, addresses an open problem for the evaluation of systems, where overall user satisfcation depends not only on the performance of the parts, but also on their combination, and on user behavior. Based on the PARADISE method known from spoken dialog systems, we have defined and applied internal and external metrics for fine-grained and largely automatable identification of such relationships. Through evaluation with n=28 subjects, indicator functions explaining up to 55% of variation in several satisfaction metrics were found. Furthermore, we demonstrate that the system’s interaction style reduces the need for instruction and successfully recovers partial failures

The Curious Robot - Structuring Interactive Robot Learning

Lütkebohle I, Peltason J, Schillingmann L, et al. The Curious Robot - Structuring Interactive Robot Learning. In: International Conference on Robotics and Automation. IEEE; 2009: 4156-4162.If robots are to succeed in novel tasks, they must be able to learn from humans. To improve such human-robot interaction, a system is presented that provides dialog structure and engages the human in an exploratory teaching scenario. Thereby, we specifically target untrained users, who are supported by mixed-initiative interaction using verbal and non-verbal modalities. We present the principles of dialog structuring based on an object learning and manipulation scenario. System development is following an interactive evaluation approach and we will present both an extensible, event-based interaction architecture to realize mixed-initiative and evaluation results based on a video-study of the system. We show that users benefit from the provided dialog structure to result in predictable and successful human-robot interaction

Mixed Initiative in Interactive Robotic Learning

Peltason J, Lütkebohle I, Wrede B, Hanheide M. Mixed Initiative in Interactive Robotic Learning. In: Workshop on Improving Human-Robot Communication with Mixed-Initiative and Context-Awareness. 2009.In learning tasks, interaction is mostly about the exchange of knowledge. The interaction process shall be governed on the one hand by the knowledge the tutor wants to convey and on the other by the lacks of knowledge of the learner. In human-robot interaction (HRI), it is usually the human demonstrating or explicitly verbalizing her knowledge and the robot acquiring a respective representation. The ultimate goal in interactive robot learning is thus to enable inexperienced, untrained users to tutor robots in a most natural and intuitive manner. This goal is often impeded by a lack of knowledge of the human about the internal processing and expectations of the robot and by the inflexibility of the robot to understand open-ended, unconstrained tutoring or demonstration. Hence, we propose mixed-initiative strategies to allow both to mutually contribute to the interactive learning process as a bi-directional negotiation about knowledge. Along this line this paper discusses two initially different case studies on object manipulation and learning of spatial environments. We present different styles of mixed-initiative in these scenarios and discuss the merits in each case