Perspective Chapter: European Robotics League – Benchmarking through Smart City Robot Competitions

The SciRoc project, started in 2018, is an EU-H2020 funded project supporting the European Robotics League (ERL) and builds on the success of the EU-FP7/H2020 projects RoCKIn, euRathlon, EuRoC and ROCKEU2. The ERL is a framework for robot competitions currently consisting of three challenges: ERL Consumer, ERL Professional and ERL Emergency. These three challenge scenarios are set up in urban environments and converge every two years under one major tournament: the ERL Smart Cities Challenge. Smart cities are a new urban innovation paradigm promoting the use of advanced technologies to improve citizens’ quality of life. A key novelty of the SciRoc project is the ERL Smart Cities Challenge, which aims to show how robots will integrate into the cities of the future as physical agents. The SciRoc Project ran two such ERL Smart Cities Challenges, the first in Milton Keynes, UK (2019) and the second in Bologna, Italy (2021). In this chapter we evaluate the three challenges of the ERL, explain why the SciRoc project introduced a fourth challenge to bring robot benchmarking to Smart Cities and outline the process in conducting a Smart City event under the ERL umbrella. These innovations may pave the way for easier robotic benchmarking in the future

Improving Sample Efficiency in Behavior Learning by Using Sub-optimal Planners for Robots

The design and implementation of behaviors for robots operating in dynamic and complex environments are becoming mandatory in nowadays applications. Reinforcement learning is consistently showing remarkable results in learning effective action policies and in achieving super-human performance in various tasks -- without exploiting prior knowledge. However, in robotics, the use of purely learning-based techniques is still subject to strong limitations. Foremost, sample efficiency. Such techniques, in fact, are known to require large training datasets, and long training sessions, in order to develop effective action policies. Hence in this paper, to alleviate such constraint, and to allow learning in such robotic scenarios, we introduce SErP (Sample Efficient robot Policies), an iterative algorithm to improve the sample-efficiency of learning algorithms. SErP exploits a sub-optimal planner (here implemented with a monitor-replanning algorithm) to lead the exploration of the learning agent through its initial iterations. Intuitively, SErP exploits the planner as an expert in order to enable focused exploration and to avoid portions of the search space that are not effective to solve the task of the robot. Finally, to confirm our insights and to show the improvements that SErP carries with, we report the results obtained in two different robotic scenarios: (1) a cartpole scenario and (2) a soccer-robots scenario within the RoboCup@Soccer SPL environment

Game Strategies for Physical Robot Soccer Players: A Survey

Effective team strategies and joint decision-making processes are fundamental in modern robotic applications, where multiple units have to cooperate to achieve a common goal. The research community in artificial intelligence and robotics has launched robotic competitions to promote research and validate new approaches, by providing robust benchmarks to evaluate all the components of a multiagent system—ranging from hardware to high-level strategy learning. Among these competitions RoboCup has a prominent role, running one of the first worldwide multirobot competition (in the late 1990s), challenging researchers to develop robotic systems able to compete in the game of soccer. Robotic soccer teams are complex multirobot systems, where each unit shows individual skills, and solid teamwork by exchanging information about their local perceptions and intentions. In this survey, we dive into the techniques developed within the RoboCup framework by analyzing and commenting on them in detail. We highlight significant trends in the research conducted in the field and to provide commentaries and insights, about challenges and achievements in generating decision-making processes for multirobot adversarial scenarios. As an outcome, we provide an overview a body of work that lies at the intersection of three disciplines: Artificial intelligence, robotics, and games

Questioning Items’ Link in Users’ Perception of a Training Robot for Elders

Socially Assistive robots are becoming more common in modern society. These robots can accomplish a variety of tasks for people that are exposed to isolation and difficulties. Among those, elderly people are the largest part, and with them, robotics can play new roles. Elderly people are the ones who usually suffer a major technological gap, and it is worth evaluating their perception when dealing with robots. To this end, the present work addresses the interaction of elderly people during a training session with a humanoid robot. The analysis has been carried out by means of a questionnaire, using four key factors: Motivation, Usability, Likability, and Sociability. The results can contribute to the design and the development of social interaction between robots and humans in training contexts to enhance the effectiveness of human-robot interaction

Learning from the Crowd: Improving the Decision Making Process in Robot Soccer using the Audience Noise

Fan input and support is an important component in many individual and team sports, ranging from athletics to basketball. Audience interaction provides a consistent impact on the athletes’ performance. The analysis of the crowd noise can provide a global indication on the ongoing game situation, less conditioned by subjective factors that can influence a single fan. In this work, we exploit the collective intelligence of the audience of a robot soccer match to improve the performance of the robot players. In particular, audio features extracted from the crowd noise are used in a Reinforcement Learning process to possibly modify the game strategy. The effectiveness of the proposed approach is demonstrated by experiments on registered crowd noise samples from several past RoboCup SPL matches

Learning a Symbolic Planning Domain through the Interaction with Continuous Environments

One of the main challenges in AI is performing dynamic tasks by using approaches that efficiently predict the environment’s future outcomes. State-of-the-art planners can reason effectively with symbolic representations of the environment. However, when the environment is continuous and unstructured, manually extracting an ad-hoc symbolic model to perform planning may be infeasible. Deep Reinforcement Learning is known to automatically learn compact representations of the state space through interaction with the environment. However, it is not suitable for planning, giving up the efficiency we would gain by predicting the consequences of actions. This work focuses on continuous state-space MDPs and proposes an approach that naturally combines interaction, symbolic representation learning, and symbolic online planning. Our system leverages experience-data gained from the environment to autonomously learn a symbolic planning model composed of: (1) a symbol grounding model to switch from continuous to symbolic space and vice versa; (2) a symbolic transition model; (3) a value function for symbolic states. This model is used at training time to lead the interaction with the world. At each interaction step, we perform fast symbolic online planning over a finite horizon to choose the action to execute in the environment. The success of this strategy in the environment implicitly validates our automatically extracted symbolic model, since the system is able to effectively plan actions in the original MDP by reasoning only in the finite and symbolic domain. The approach has been evaluated on several continual OpenAI gym environments, addressing successfully both control problems and games

Nothing About Us Without Us: a participatory design for an Inclusive Signing Tiago Robot

The success of the interaction between the robotics community and the users of these services is an aspect of considerable importance in the drafting of the development plan of any technology. This aspect becomes even more relevant when dealing with sensitive services and issues such as those related to interaction with specific subgroups of any population. Over the years, there have been few successes in integrating and proposing technologies related to deafness and sign language. Instead, in this paper, we propose an account of successful interaction between a signatory robot and the Italian deaf community, which occurred during the Smart City Robotics Challenge (SciRoc) 2021 competition 1. Thanks to the use of a participatory design and the involvement of experts belonging to the deaf community from the early stages of the project, it was possible to create a technology that has achieved significant results in terms of acceptance by the community itself and could lead to significant results in the technology development as well