Robotics researchers have been focusing on developing autonomous and human-like intelligent robots that are able to plan, navigate, manipulate objects, and interact with humans in both static and dynamic environments. These capabilities, however, are usually developed for direct interactions with people in controlled environments, and evaluated primarily in terms of human safety. Consequently, human-robot interaction (HRI) in scenarios with no intervention of technical personnel is under-explored. However, in the future, robots will be deployed in unstructured and unsupervised environments where they will be expected to work unsupervised on tasks which require direct interaction with humans and may not necessarily be collaborative. Developing such robots requires comparing the effectiveness and efficiency of similar design approaches and techniques. Yet, issues regarding the reproducibility of results, comparing different approaches between research groups, and creating challenging milestones to measure performance and development over time make this difficult. Here we discuss the international robotics competition called RoboCup as a benchmark for the progress and open challenges in AI and robotics development. The long term goal of RoboCup is developing a robot soccer team that can win against the world’s best human soccer team by 2050. We selected RoboCup because it requires robots to be able to play with and against humans in unstructured environments, such as uneven fields and natural lighting conditions, and it challenges the known accepted dynamics in HRI. Considering the current state of robotics technology, RoboCup’s goal opens up several open research questions to be addressed by roboticists. In this paper, we (a) summarise the current challenges in robotics by using RoboCup development as an evaluation metric, (b) discuss the state-of-the-art approaches to these challenges and how they currently apply to RoboCup, and (c) present a path for future development in the given areas to meet RoboCup’s goal of having robots play soccer against and with humans by 2050.

One of the fundamental modes of learning in children is through curiosity. Children (and adults) interact with new people, learn about novel objects, activities and other stimuli through curiosity and other intrinsic motivations. Creating autonomous robots that learn continually through intrinsic curiosity may result in breakthroughs in artificial intelligence. Such robots could continue to learn about themselves and the world around them through curiosity, thus improving their abilities over their ‘lifetime’. Although recent works on curiosity in different fields have produced significant results, most of these works have focused on constrained simulated environments which do not involve human interaction. However, in real-world applications such as healthcare, home-assistance etc., robots generally have to interact with humans on a regular basis. In these scenarios, it is imperative that curiosity is directed towards seeking out and learning important information from the humans when needed rather than simply learning in an unsupervised manner. Further, there is limited work on how humans perceive such curious robots and whether humans prefer curious robots that adapt over time to other robots that simply perform their assigned tasks. In this workshop, our goal is to bring together researchers and practitioners in different multidisciplinary fields to discuss the role of robot curiosity in real-world applications and its implications in human-robot interaction (HRI).

The word “robot” frequently conjures unrealistic expectations of utilitarian perfection: tireless, efficient and flawless agents. However, real-world robots are far from perfect—they fail and make mistakes. Thus, roboticists should consider altering their current assumptions and cultivating new perspectives that account for a more complete range of robot roles, behaviors, and interactions. To encourage this, we explore the use of metaphors for generating novel ideas and reframing existing problems, eliciting new perspectives of human-robot interaction. Our work makes two contributions. We (1) surface current assumptions that accompany the term “robots,” and (2) present a collection of alternative perspectives of interaction with robots through metaphors. By identifying assumptions, we provide a comprehensible list of aspects to reconsider regarding robots’ physicality, roles, and behaviors. Through metaphors, we propose new ways of examining how we can use, relate to, and co-exist with the robots that will share our future.

This workshop focused on identifying the challenges and dynamics between people and robots to foster short interactions and long-lasting relationships in different fields, from educational, service, collaborative, companion, care-home and medical robotics. For that, this workshop facilitated a discussion about people's trust towards robots "in the field", inviting workshop participants to contribute their past experiences and lessons learnt.

Effective and successful interactions between robots and people are possible only when they both are able to infer the other's intentions, beliefs, and goals. In particular, robots' mental models need to be transparent to be accepted by people and facilitate the collaborations between the involved parties. In this study, we focus on investigating how to create legible emotional robots' behaviours to be used to make their decision-making process more transparent to people. In particular, we used emotions to express the robot's internal status and feedback during an interactive learning process. We involved 28 participants in an online study where they rated the robot's behaviours, designed in terms of colours, icons, movements and gestures, according to the perceived intention and emotions.

A challenge in using fully autonomous robots in human-robot interaction (HRI) is to design behavior that is engaging enough to encourage voluntary, long-term interaction, yet robust to the perturbations induced by human interaction. It has been repeatedly argued that intrinsic motivations (IMs) are crucial for human development, so it seems reasonable that this mechanism could produce an adaptive and developing robot, which is interesting to interact with. This thesis evaluates whether an intrinsically motivated robot can lead to sustained HRI. Recent research showed that robots which ‘appeared’ intrinsically motivated raised interest in the human interaction partner. The displayed IMs resulted from ‘unpredictably’ asking a question or from a self-disclosing statement. They were designed with the help of pre-defined scripts or teleoperation. An issue here is that this practice renders the behavior less robust toward unexpected input or requires a trained human in the loop. Instead, this thesis proposes a computational model of IM to realize fully autonomous and adaptive behavior generation in a robot. Previous work showed that predictive information maximization leads to playful, exploratory behavior in simulated robots that is robust to changes in the robot’s morphology and environment. This thesis demonstrates how to deploy the formalism on a physical robot that interacts with humans. The thesis conducted three within-subjects studies, where participants interacted with a fully autonomous Sphero BB8 robot with two behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive. The first study contributes to the idea of the overall proposed study design: the interaction needs to be designed in such a way, that participants are not given any idea of the robot’s task. The second study implements this idea, letting participants focus on answering the question of whether the robots are any different. It further contributes ideas for a more ‘challenging’ baseline behavior motivating the third and final study. Here, a systematic baseline is generated and shows that participants perceive it as almost indistinguishable and similarly animated compared to the intrinsically motivated robot. Despite the emphasis on the design of similarly perceived baseline behaviors, quantitative analyses of post-interaction questionnaires after each study showed a significantly higher perception of the dimension ‘Warmth’ for the intrinsically motivated robot compared to the baseline behavior. Warmth is considered a primary dimension for social attitude formation in social cognition. A human perceived as warm (i.e. friendly and trustworthy) experiences more positive social interactions. The Robotic Social Attribute Scale (RoSAS) implements the scale dimension Warmth for the HRI domain, which has been validated with a series of still images. Going beyond static images, this thesis provides support for the use and applicability of this scale dimension for the purpose of…

A solid methodology to understand human perception and preferences in human-robot interaction (HRI) is crucial in designing real-world HRI. Social cognition posits that the dimensions Warmth and Competence are central and universal dimensions characterizing other humans [1]. The Robotic Social Attribute Scale (RoSAS) proposes items for those dimensions suitable for HRI and validated them in a visual observation study. In this paper we complement the validation by showing the usability of these dimensions in a behavior based, physical HRI study with a fully autonomous robot. We compare the findings with the popular Godspeed dimensions Animacy, Anthropomorphism, Likeability, Perceived Intelligence and Perceived Safety. We found that Warmth and Competence, among all RoSAS and Godspeed dimensions, are the most important predictors for human preferences between different robot behaviors. This predictive power holds even when there is no clear consensus preference or significant factor difference between conditions.

Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable outside of this domain are lacking. We propose an approach conceptually different from those taken previously. It is based on semantic segmentation and does achieve these desired properties. In detail, it is being able to process full VGA images in real-time on a low-power mobile processor. It can further handle multiple image dimensions without retraining, it does not require specific domain knowledge to achieve a high frame rate and it is applicable on a minimal mobile hardware.

We participated in the RoboCup 2018 competition in Montreal with our newly developed BoldBot based on the Darwin-OP and mostly self-printed custom parts. This paper is about the lessons learnt from that competition and further developments for the RoboCup 2019 competition. Firstly, we briefly introduce the team along with an overview of past achievements. We then present a simple, standalone 2D simulator we use for simplifying the entry for new members with making basic RoboCup concepts quickly accessible. We describe our approach for semantic-segmentation for our vision used in the 2018 competition, which replaced the lookup-table (LUT) implementation we had before. We also discuss the extra structural support we plan to add to the printed parts of the BoldBot and our transition to ROS 2 as our new middleware. Lastly, we will present a collection of open-source contributions of our team.

A challenge in using robots in human-inhabited environments is to design behavior that is engaging, yet robust to the perturbations induced by human interaction. Our idea is to imbue the robot with intrinsic motivation (IM) so that it can handle new situations and appears as a genuine social other to humans and thus be of more interest to a human interaction partner. Human-robot interaction (HRI) experiments mainly focus on scripted or teleoperated robots, that mimic characteristics such as IM to control isolated behavior factors. This article presents a "robotologist" study design that allows comparing autonomously generated behaviors with each other and, for the first time, evaluates the human perception of IM-based generated behavior in robots. We conducted a within-subjects user study (N = 24) where participants interacted with a fully autonomous Sphero BB8 robot with different behavioral regimes: one realizing an adaptive, intrinsically motivated behavior and the other being reactive, but not adaptive. The robot and its behaviors are intentionally kept minimal to concentrate on the effect induced by IM. A quantitative analysis of post-interaction questionnaires showed a significantly higher perception of the dimension "Warmth" compared to the reactive baseline behavior. Warmth is considered a primary dimension for social attitude formation in human social cognition. A human perceived as warm (friendly, trustworthy) experiences more positive social interactions.

There has always been much motivation for sharing code and solutions among teams in the RoboCup community. Yet the transfer of code between teams was usually complicated due to a huge variety of used frameworks and their differences in processing sensory information. The RoboCup@Home league has tackled this by transitioning to ROS as a common framework. In contrast, other leagues, such as those using humanoid robots, are reluctant to use ROS, as in those leagues real-time processing and low-computational complexity is crucial. However, ROS 2 now offers built-in support for real-time processing and promises to be suitable for embedded systems and multi-robot systems. It also offers the possibility to compose a set of nodes needed to run a robot into a single process. This, as we will show, reduces communication overhead and allows to have one single binary, which is pertinent to competitions such as the 3D-Simulation League. Although ROS 2 has not yet been announced to be production ready, we started the process to develop ROS 2 packages for using it with humanoid robots (real and simulated). This paper presents the developed modules, our contributions to ROS 2 core and RoboCup related packages, and most importantly it provides benchmarks that indicate that ROS 2 is a promising candidate for a common framework used among leagues.

It remains one of the main challenges for mobile robots in a human inhabited environment to interact with surrounding individuals. Usually, recognizing touch gestures, perceiving proximity information, or distinguishing between individuals is both a cost and configuration intensive task. In this demonstration we utilize inexpensive Bluetooth Low Energy (BLE) devices attached to the robot and participants. We derive information of participants by measuring the received signal strength (RSS) between BLE devices. The system’s flexibility is shown by using a proprietary mobile spherical robot. Three proof-of-concept demonstrations will be presented, each refers to one information factor about individuals in the surrounding environment: A) proximity, B) touch, and C) who is interacting with the robot. The audience can observe the robot’s output data of the raw RSS to surrounding BLE devices attached to wristbands worn by interacting participants.

This demonstration shows how inexpensive, off-the-shelf, and unobtrusive Bluetooth Low Energy (BLE) devices can be utilized for enabling robots to recognize touch gestures, to perceive proximity information, and to distinguish between interacting individuals autonomously. The received signal strength (RSS) between the BLE device attached to the robot and BLE devices attached to the interacting individuals is used to achieve this. Almost no software configuration is needed and the setup can be applied to most everyday environments and robot platforms.

. In this paper we describe the RoboCup Humanoid Kid Size division of team Bold Hearts, the RoboCup team of the University of Hertfordshire, in Hatfield UK. We discuss construction and aspects of our software architecture, as well as some of our solutions to important problems such as action selection, vision, stability, and sensorimotor modeling. Finally, we provide an overview of the relevant experience and achievements of our team.

—Information about objects and their positions in an environment are necessary requirements for most tasks of a mobile autonomous robot, in particular regarding the control of behavior and navigation. This presents a specialchallenge for robots with a limited view angle. Autonomously soccer playing robots in the dynamic environment of the RoboCup Standard Platform League are exposed to these difficulties. Most approaches aggregate all available information in one holistic model in order to localize robots. In case of inconsistent perceptions the model either turns noisy or creates and tracks an addiotional hypothesis. To improve the localization – and thus the behavior control – local models have received only little attention so far. In this work the implementation of a local goal model is presented and analyzed. A multi-hypothesis particle filter is used to cope with ambiguity of goal post percepts as well as to process incomplete and uncertain sensor information. Additionally, a percept buffer supports the initialization and also facilitates the handling of sparse false measurements. On the basis of this local goal model inconsistencies can be explicitly modeled, which may be used to stabilize the location of a robot.

The ability to grasp objects of different size and shape is one of the most important skills of a humanoid robot. Human grasping integrates a lot of different senses. In particular, the tactile sensing is very important for a stable grasping motion. When we lift a box without knowing what is inside, we do it carefully using our tactile and proprioceptive senses to estimate the weight and thus, the force necessary to hold and to lift this box. In this paper we present an adaptive controlling mechanism which enables a robot to grasp objects of different weights. Thereby, we only use the proprioceptive sensors like positions and electric current at the joints and force sensors at the end-effectors providing the robot with tactile feedback. We implemented and tested our approach on a humanoid robot.

Strategic positioning is a decisive part of the team play within a soccer game. In most solutions the positioning techniques are treated as a constituent of a complete team play strategy. In a comprehensive overview we discuss the team play and positioning methods used within RoboCup and extract the essential requirements for player positioning. In this work, we propose an approach for strategic positioning allowing for flexible formulation of arbitrary strategies. Based on the conditions of a specific strategy, the field is subdivided in regions by a Voronoi tessellation and each region is assigned a weight. Those weights influence the calculation of the optimal robot position as well as the path. A team play strategy can be expressed by the choice of the tessellation as well as the choice of the weights. This provides a powerful abstraction layer simplifying the design of the actual play strategy. We also present an implementation of an example strategy based on this approach and analyze the performance of our approach in simulation.

The research group Nao-Team Humboldt was founded at the end of 2007 and consists of students and researchers at the Humboldt-Universität zu Berlin, coming from a number of different countries during the last years, in particular from Germany, Italy, Russia, Egypt, China, Iraq, and Iran. The team is a part of the AI research Lab of the Humboldt-Universität which is headed by Prof. Dr. Hans-Dieter Burkhard and is led by Heinrich Mellmann. At the current state we have 2 Phd students and around 10 Master Students in the core team. Additionally we provide courses and seminars where the students solve tasks related to RoboCup. We have a long tradition within the RoboCup by working for the Four Legged league as a part of the GermanTeam in recent years, where we won the competition three times. The current team members are: Heinrich Mellmann, Yuan Xu, Thomas Krause, Claas-Norman Ritter, Florian Holzhauer, Marcus Scheunemann, Paul Schütte, Martin Schneider, Kirill Yasinovskiy, Luisa Jahn, Dominik Krienelke, Christian Rekittke. We started with Naos in May 2008 and achieved the 4. place at the competition in Suzhou 2008. We achieved 3. place in technical challenge at Graz 2009. In 2010, we won the 2. place at RomeCup/Rom, 1. Place in Athens/Greece, and 4. place at German Open 2010. In 2010 we first time participated simultaneously in SPL in Simulation 3D with the same code. In the 3D Simulation we won the German Open and the AutCup competitions and achieved the 2. place at the RoboCup World Championship 2010 in Singapore. In 2011 we won the Iran Open competition in SPL and got 4. place in Simulation 3D, at the German Open we got 4. place as well. With our efforts in both leagues, we hope to foster the cooperation between the two leagues and to improve both of them. We also cooperate with several other teams and associate projects at different universities, e.g., we regularly make test games with the humanoid team FUManoids. Our general research fields include agent oriented techniques and machine learning with their applications to cognitive robotics. As our record shows, our results are recognized outside RoboCup as well. Our current research focuses among others mainly on the following topics: