Scientific contributions

We present the main scientific contributions achieved by the two research groups and the research topics relevant to the RoboCup@Home competitions and outline how they will contribute and be further developed in this context.

The teams contribute complementary achievements so that their integration will allow for increased robustness and effectiveness of the proposed solutions. We thus believe that our RoboCup@Home project will be the basis to achieve additional novel results on the topics mentioned below.

• Planning and plan execution

The deployment of robots in populated environments interacting with non-expert users requires facing many sources of uncertainty during task execution such as incomplete information about the environment or unpredictable behaviours coming from humans. Planning and plan execution under such uncertainties is also an important problem to be addressed within the RoboCup@Home competition and in this context, both Sapienza and Lincoln partners have recent research results.

In [Sanelli-ICAPS17], we propose a method using conditional planning for generating and executing short-term interactions, in [Sebastiani-ICAPS17], we propose an extension to the Hierarchical Agent-based Task Planner (HATP) for the automatic generation of conditional plans that enables humans and robots to negotiate some aspects of the collaboration online during the execution of the plan and, in [Hanheide-AI15], task failure is handled intelligently by combining different types of robot’s knowledge to solve the problem of task planning and execution under uncertainty and in open worlds, explanation of task failure and verification of those explanations.

• Human-Robot Interaction

Social robots deployed in large public spaces have to carry out short-term interactions with many unknown people. In order to provide a better user experience, personalized multi-modal interactions have shown to be more effective. In this context, [Iocchi-ICSR15] presented a module for HRI based on explicit representation of social norms that provides a high degree of variability in the personalization of the interactions, maintaining easy extendibility and scalability.

Generating appropriate robot behaviours during the interaction it is also a key factor to achieve successful interactions. In [Dondrup-Rob15], the problem of maintaining Human-Robot Spatial Interactions (HRSI) is studied from the point of view of the Proxemics, where distances between the agents are included into a probabilistic model based on a Qualitative Trajectory Calculus.

Finally, analysing the performance of an HRI system in order to improve the interactions requires a systematic approach. In [Lohse-HRI09], a method is proposed to jointly analyse system level and interaction level in an integrated manner. The approach allows to trace back patterns that deviate from prototypical interaction sequences to the distinct system components of the robot.

• Long-term autonomy

One of the main goals of the RoboCup@Home is to develop a system able to robustly navigate in dynamic environments subject to changes and unpredictable situations. In this context, [Pulido-IROS16] presented a localization and mapping system based on a spatio-temporal occupancy grid that explicitly represents the persistence and periodicity of the individual cells and can predict the probability of their occupancy in the future. The proposed representation improves localisation accuracy and the efficiency of path planning.

In [Pulido-ICRA15], we present an approach for topological navigation of service robots in dynamic indoor environments this approach uses a topological representation of the environment that simplifies definition of navigation actions, and is augmented with a spatio-temporal model that specifically represent changes that stem from events in the environment, which impact on the success probability of planned actions which allows the robot to predict action outcomes and to devise better navigation plans.

In [Hanheide-HRI17], we have also shown how better HRI can be facilitated by exploiting long-term spatio-temporal experience, similar to the approached above, but directly linking long-term autonomy with setting goals for a mobile robot.

In populated environments, the ability to be able to predict the directions people are heading is useful for robots to plan suitable path. The machine learning method in [Sun-arxiv17] allows to learn a model for such predictions from long-term experience.

Publications

• [Sanelli-ICAPS17] V. Sanelli, M. Cashmore, D. Magazzeni, and L. Iocchi. Short-Term Human Robot Interaction through Conditional Planning and Execution. In Proc. of International Conference on Automated Planning and Scheduling (ICAPS), 2017. PDF
• [Sebastiani-ICAPS17] E. Sebastiani, R. Lallement, R. Alami, and L. Iocchi. Dealing with On-line Human-Robot Negotiations in Hierarchical Agent-based Task Planner. In Proc. of International Conference on Automated Planning and Scheduling (ICAPS), 2017. PDF
• [Hanheide-AI15] M. Hanheide et al. Robot task planning and explanation in open and uncertain worlds. Artificial Intelligence, 2015. DOI
• [Iocchi-ICSR15] L. Iocchi, M. T. Lázaro, L. Jeanpierre, and A.-I. Mouaddib. Personalized short-term multi-modal interaction for social robots assisting users in shopping malls. In Int. Conf. on Social Robotics, Paris, France, Oct 26-30 2015. PDF
• [Dondrup-Rob15] C. Dondrup, N. Bellotto, M. Hanheide, K. Eder, and U. Leonards. A computational model of human-robot spatial interactions based on a qualitative trajectory calculus. Robotics, 4(1):63, 2015. DOI
• [Lohse-HRI09] M. Lohse, M. Hanheide, K. J. Rohlfing, and G. Sagerer. Systemic interaction analysis (SInA) in HRI. In The 4th ACM/IEEE Int. Conf. on Human Robot Interaction, HRI ’09, pages 93–100, New York, NY, USA, 2009. ACM. DOI
• [Pulido-IROS16] J. Pulido Fentanes, T. Krajnik, M. Hanheide, and T. Duckett. Persistent localization and life-long mapping in changing environments using the frequency map enhancement. In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Daejeon, Korea, Oct. 9 - 14 2016. DOI
• [Pulido-ICRA15] J. Pulido Fentanes, B. Lacerda, T. Krajnik, N. Hawes, and M. Hanheide. Now or later? predicting and maximising success of navigation actions from long-term experience. In Int. Conf. on Robotics and Automation (ICRA), 2015. DOI
• [Hanheide-HRI17] M. Hanheide, D. Hebesberger, and T. Krajnik. The When, Where, and How: An Adaptive Robotic Info-Terminal for Care Home Residents – A long-term Study. In Proc ACM/IEEE Int. Conf. on Human-Robot Interaction (HRI), Vienna, 2017. DOI
• [Sun-arxiv17] Li Sun, Zhi Yan, Sergi Molina Mellado, Marc Hanheide, and Tom Duckett. 3dof pedestrian trajectory prediction learned from long-term autonomous mobile robot deployment data. arXiv preprint arXiv:1710.00126, 2017.