The human in the loop Perspectives and challenges for RoboCup 2050

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.</p

The human in the loop Perspectives and challenges for RoboCup 2050

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/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.Peer reviewe

The Robotic Visual Information Processing System Based on Wavelet Transformation and Photoelectric Hybrid

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An Architecture for Multilevel Learning and Robotic Control based on Concept Generation

Robot and multi-robot systems are inherently complex systems, for which designing the programs to control their behaviours proves complicated. Moreover, control programs that have been successfully designed for a particular environment and task can become useless if either of these change. It is for this reason that this thesis investigates the use of machine learning within robot and multi-robot systems. It explores an architecture for machine learning, applied to autonomous mobile robots based on dividing the learning task into two individual but interleaved sub-tasks. The first sub-task consists of finding an appropriate representation on which to base behaviour learning. The thesis explores the viability of using multidimensional classification techniques to generalise the original sensor and motor representations into abstract hierarchies of 'concepts'. To construct concepts the research used standard classification techniques, and experimented with a novel method of multidimensional data classification based on 'Q-analysis'. Results suggest that this may be a powerful new approach to concept learning. The second sub-task consists of using the previously acquired concepts as the representation for behaviour learning. The thesis explores whether it is possible to learn robotic behaviours represented using concepts. Results show that is possible to learn low-level behaviours such as navigation and higher-level ones such as ball passing in robot football. The thesis concludes that the proposed architecture is viable for robotic behaviour learning and control, and that incorporating Q-analysis based classification results in a promising new approach to the control of robot and multi-robot systems

Aprendizagem de coordenação em sistemas multi-agente

The ability for an agent to coordinate with others within a system is a valuable property in multi-agent systems. Agents either cooperate as a team to accomplish a common goal, or adapt to opponents to complete different goals without being exploited. Research has shown that learning multi-agent coordination is significantly more complex than learning policies in singleagent environments, and requires a variety of techniques to deal with the properties of a system where agents learn concurrently. This thesis aims to determine how can machine learning be used to achieve coordination within a multi-agent system. It asks what techniques can be used to tackle the increased complexity of such systems and their credit assignment challenges, how to achieve coordination, and how to use communication to improve the behavior of a team. Many algorithms for competitive environments are tabular-based, preventing their use with high-dimension or continuous state-spaces, and may be biased against specific equilibrium strategies. This thesis proposes multiple deep learning extensions for competitive environments, allowing algorithms to reach equilibrium strategies in complex and partially-observable environments, relying only on local information. A tabular algorithm is also extended with a new update rule that eliminates its bias against deterministic strategies. Current state-of-the-art approaches for cooperative environments rely on deep learning to handle the environment’s complexity and benefit from a centralized learning phase. Solutions that incorporate communication between agents often prevent agents from being executed in a distributed manner. This thesis proposes a multi-agent algorithm where agents learn communication protocols to compensate for local partial-observability, and remain independently executed. A centralized learning phase can incorporate additional environment information to increase the robustness and speed with which a team converges to successful policies. The algorithm outperforms current state-of-the-art approaches in a wide variety of multi-agent environments. A permutation invariant network architecture is also proposed to increase the scalability of the algorithm to large team sizes. Further research is needed to identify how can the techniques proposed in this thesis, for cooperative and competitive environments, be used in unison for mixed environments, and whether they are adequate for general artificial intelligence.A capacidade de um agente se coordenar com outros num sistema é uma propriedade valiosa em sistemas multi-agente. Agentes cooperam como uma equipa para cumprir um objetivo comum, ou adaptam-se aos oponentes de forma a completar objetivos egoístas sem serem explorados. Investigação demonstra que aprender coordenação multi-agente é significativamente mais complexo que aprender estratégias em ambientes com um único agente, e requer uma variedade de técnicas para lidar com um ambiente onde agentes aprendem simultaneamente. Esta tese procura determinar como aprendizagem automática pode ser usada para encontrar coordenação em sistemas multi-agente. O documento questiona que técnicas podem ser usadas para enfrentar a superior complexidade destes sistemas e o seu desafio de atribuição de crédito, como aprender coordenação, e como usar comunicação para melhorar o comportamento duma equipa. Múltiplos algoritmos para ambientes competitivos são tabulares, o que impede o seu uso com espaços de estado de alta-dimensão ou contínuos, e podem ter tendências contra estratégias de equilíbrio específicas. Esta tese propõe múltiplas extensões de aprendizagem profunda para ambientes competitivos, permitindo a algoritmos atingir estratégias de equilíbrio em ambientes complexos e parcialmente-observáveis, com base em apenas informação local. Um algoritmo tabular é também extendido com um novo critério de atualização que elimina a sua tendência contra estratégias determinísticas. Atuais soluções de estado-da-arte para ambientes cooperativos têm base em aprendizagem profunda para lidar com a complexidade do ambiente, e beneficiam duma fase de aprendizagem centralizada. Soluções que incorporam comunicação entre agentes frequentemente impedem os próprios de ser executados de forma distribuída. Esta tese propõe um algoritmo multi-agente onde os agentes aprendem protocolos de comunicação para compensarem por observabilidade parcial local, e continuam a ser executados de forma distribuída. Uma fase de aprendizagem centralizada pode incorporar informação adicional sobre ambiente para aumentar a robustez e velocidade com que uma equipa converge para estratégias bem-sucedidas. O algoritmo ultrapassa abordagens estado-da-arte atuais numa grande variedade de ambientes multi-agente. Uma arquitetura de rede invariante a permutações é também proposta para aumentar a escalabilidade do algoritmo para grandes equipas. Mais pesquisa é necessária para identificar como as técnicas propostas nesta tese, para ambientes cooperativos e competitivos, podem ser usadas em conjunto para ambientes mistos, e averiguar se são adequadas a inteligência artificial geral.Apoio financeiro da FCT e do FSE no âmbito do III Quadro Comunitário de ApoioPrograma Doutoral em Informátic