A systematic literature review of decision-making and control systems for autonomous and social robots

In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR

Pain and self-preservation in autonomous robots: From neurobiological models to psychiatric disease

The use of biologically realistic (brain-like) control systems in autonomous robots offers two potential benefits. For neuroscience, it may provide important insights into normal and abnormal control and decision-making in the brain, by testing whether the computational learning and decision rules proposed on the basis of simple laboratory experiments lead to effective and coherent behaviour in complex environments. For robotics, it may offer new insights into control system designs, for example in the context of threat avoidance and self-preservation. In the brain, learning and decision-making for rewards and punishments (such as pain) are thought to involve integrated systems for innate (Pavlovian) responding, habit-based learning, and goal-directed learning, and these systems have been shown to be well-described by RL models. Here, we simulated this 3-system control hierarchy (in which the innate system is derived from an evolutionary learning model), and show that it reliably achieves successful performance in a dynamic predator-avoidance task. Furthermore, we show situations in which a 3-system architecture provides clear advantages over single or dual system architectures. Finally, we show that simulating a computational model of obsessive compulsive disorder, an example of a disease thought to involve a specific deficit in the integration of habit-based and goal-directed systems, can reproduce the results of human clinical experiments. The results illustrate how robotics can provide a valuable platform to test the validity and utility of computational models of human behaviour, in both health and disease. They also illustrate how bio-inspired control systems might usefully inform self-preservative behaviour in autonomous robots, both in normal and malfunctioning situations

An Emotion Theory Approach to Artificial Emotion Systems for Robots and Intelligent Systems: Survey and Classification

This is the published version.To assist in the evaluation process when determining architectures for new robots and intelligent systems equipped with artificial emotions, it is beneficial to understand the systems that have been built previously. Other surveys have classified these systems on the basis of their technological features. In this survey paper, we present a classification system based on a model similar to that used in psychology and philosophy for theories of emotion. This makes possible a connection to thousands of years of discourse on the topic of emotion. Five theories of emotion are described based on an emotion theory model proposed by Power and Dalgleish. The paper provides classifications using a model of 10 new questions, for 14 major research projects that describe implementations or designs for systems that use artificial emotions for either robotics or general artificial intelligenc

Dynamic Behavior Sequencing in a Hybrid Robot Architecture

Hybrid robot control architectures separate plans, coordination, and actions into separate processing layers to provide deliberative and reactive functionality. This approach promotes more complex systems that perform well in goal-oriented and dynamic environments. In various architectures, the connections and contents of the functional layers are tightly coupled so system updates and changes require major changes throughout the system. This work proposes an abstract behavior representation, a dynamic behavior hierarchy generation algorithm, and an architecture design to reduce this major change incorporation process. The behavior representation provides an abstract interface for loose coupling of behavior planning and execution components. The hierarchy generation algorithm utilizes the interface allowing dynamic sequencing of behaviors based on behavior descriptions and system objectives without knowledge of the low-level implementation or the high-level goals the behaviors achieve. This is accomplished within the proposed architecture design, which is based on the Three Layer Architecture (TLA) paradigm. The design provides functional decomposition of system components with respect to levels of abstraction and temporal complexity. The layers and components within this architecture are independent of surrounding components and are coupled only by the linking mechanisms that the individual components and layers allow. The experiments in this thesis demonstrate that the: 1) behavior representation provides an interface for describing a behavior’s functionality without restricting or dictating its actual implementation; 2) hierarchy generation algorithm utilizes the representation interface for accomplishing high-level tasks through dynamic behavior sequencing; 3) representation, control logic, and architecture design create a loose coupling, but defined link, between the planning and behavior execution layer of the hybrid architecture, which creates a system-of-systems implementation that requires minimal reprogramming for system modifications

A Reference Software Architecture for Social Robots

Social Robotics poses tough challenges to software designers who are required to take care of difficult architectural drivers like acceptability, trust of robots as well as to guarantee that robots establish a personalised interaction with their users. Moreover, in this context recurrent software design issues such as ensuring interoperability, improving reusability and customizability of software components also arise. Designing and implementing social robotic software architectures is a time-intensive activity requiring multi-disciplinary expertise: this makes difficult to rapidly develop, customise, and personalise robotic solutions. These challenges may be mitigated at design time by choosing certain architectural styles, implementing specific architectural patterns and using particular technologies. Leveraging on our experience in the MARIO project, in this paper we propose a series of principles that social robots may benefit from. These principles lay also the foundations for the design of a reference software architecture for Social Robots. The ultimate goal of this work is to establish a common ground based on a reference software architecture to allow to easily reuse robotic software components in order to rapidly develop, implement, and personalise Social Robots