ToBI - Team of Bielefeld A Human-Robot Interaction System for RoboCup@Home 2017

Wachsmuth S, Lier F, Meyer zu Borgsen S, Kummert J, Lach L, Sixt D. ToBI - Team of Bielefeld A Human-Robot Interaction System for RoboCup@Home 2017. Presented at the RoboCup 2017, Nagoya.The Team of Bielefeld (ToBI) has been founded in 2009. The RoboCup teams’ activities are embedded in a long-term research agenda towards human-robot interaction with laypersons in regular and smart home environments. The RoboCup@Home competition is an im- portant benchmark and milestone for this goal in terms of robot capabilities as well as the system integration effort. In order to achieve a robust and stable system performance, we apply a systematic approach for reproducible robotic experimentation including automatic tests. For RoboCup 2017, we plan to enhance this approach by simulating complete RoboCup@Home tasks. We further extend it to the RoboCup@Home standard platform Pepper. Similar to the Nao platform, the Pepper comes with its own runtime and development eco-system. Thus, one of the chal- lenges will be the cross-platform transfer of capabilities between robots based on different eco-system, e.g. the utilized middleware and application layers. In this paper, we will present a generic approach to such issues: the Cognitive Interaction Toolkit. The overall framework inherently supports the idea of open research and offers direct access to reusable components and reproducible systems via a web-based catalog. A main focus of research at Bielefeld are robots as an ambient host in a smart home or for instance as a museum’s guide. Both scenarios are highly relevant for the RoboCup@Home standard platform competition. Skills developed in these domains will be transferred to the RoboCup@Home scenarios

Metric Learning with Self-Adjusting Memory for Explaining Feature Drift

Kummert J, Schulz A, Hammer B. Metric Learning with Self-Adjusting Memory for Explaining Feature Drift. SN Computer Science. 2023;4(4): 376

Efficient Reject Options for Particle Filter Object Tracking in Medical Applications

Kummert J, Schulz A, Redick T, et al. Efficient Reject Options for Particle Filter Object Tracking in Medical Applications. Sensors. 2021;21(6): 2114.Reliable object tracking that is based on video data constitutes an important challenge in diverse areas, including, among others, assisted surgery. Particle filtering offers a state-of-the-art technology for this challenge. Becaise a particle filter is based on a probabilistic model, it provides explicit likelihood values; in theory, the question of whether an object is reliably tracked can be addressed based on these values, provided that the estimates are correct. In this contribution, we investigate the question of whether these likelihood values are suitable for deciding whether the tracked object has been lost. An immediate strategy uses a simple threshold value to reject settings with a likelihood that is too small. We show in an application from the medical domain—object tracking in assisted surgery in the domain of Robotic Osteotomies—that this simple threshold strategy does not provide a reliable reject option for object tracking, in particular if different settings are considered. However, it is possible to develop reliable and flexible machine learning models that predict a reject based on diverse quantities that are computed by the particle filter. Modeling the task in the form of a regression enables a flexible handling of different demands on the tracking accuracy; modeling the challenge as an ensemble of classification tasks yet surpasses the results, while offering the same flexibility

ToBI - Team of Bielefeld Enhancing the Robot Capabilities of the Social Standard Platform Pepper

Lier F, Kummert J, Renner P, Wachsmuth S. ToBI - Team of Bielefeld Enhancing the Robot Capabilities of the Social Standard Platform Pepper. In: Holz D, Genter K, Saad M, von Stryk O, eds. RoboCup 2018: Robot World Cup XXII. Lecture Notes in Computer Science. Vol 11374. Cham: Springer; 2019: 524-535

Local Reject Option for Deterministic Multi-class SVM

Kummert J, Paaßen B, Jensen J, Göpfert C, Hammer B. Local Reject Option for Deterministic Multi-class SVM. In: E.P. Villa A, Masulli P, Pons Rivero AJ, eds. Artificial Neural Networks and Machine Learning - ICANN 2016 - 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II. Lecture Notes in Computer Science. Vol 9887. Cham: Springer Nature; 2016: 251--258