Robot pain: a speculative review of its functions

Given the scarce bibliography dealing explicitly with robot pain, this chapter has enriched its review with related research works about robot behaviours and capacities in which pain could play a role. It is shown that all such roles ¿ranging from punishment to intrinsic motivation and planning knowledge¿ can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft

Emotion-based Parameter Modulation for a Mobile Robot Planning and Control System

The hypothesis that artificial emotion-like mechanisms can improve the adaptive performance of robots and intelligent systems has gained considerable support in recent years. While artificial emotions are typically employed to facilitate human-machine interaction, this thesis instead focuses on modelling emotions and affect in a non-social context. In particular, affective mechanisms are applied to the problem of mobile robot navigation. A three-layered reactive/deliberative controller is developed and implemented, resulting in several contributions to the field of mobile robot control. Rather than employing a reactive layer, a deliberative layer and an interface between them, the control problem is decomposed into three different conceptual spaces - position space, direction space and velocity space - with a distinct control layer applied to each. Existing directional and velocity space approaches such as the vector field histogram (VFH) and dynamic window methods employ different underlying mechanisms and terminology. This thesis unifies these approaches in order to compare and combine them. The weighted sum objective functions employed by some existing approaches that inspired the presented directional and velocity control layers are replaced by weighted products. This enables some hard constraints to be relaxed in favour of weighted contributions, potentially improving a system's flexibility without sacrificing safety (but coming at a cost to efficiency). An affect model is developed that conceptualises emotions and other affective interactions as modulations of cognitive processes. Unlike other models of affect-modulated cognition (e.g. Dorner and Hille, 1995), this model is designed specifically to address problems relating to mobile robot navigation. The role of affect in this model is to continuously adapt a controller's behaviour patterns in response to different environments and momentary conditions encountered by the robot. Affective constructs such as moods and emotions are represented as intensity values that arise from hard-coded interpretations of local stimuli, as well as from learned associations stored in global maps. They are expressed as modulations of control parameters and location-specific biases to path-planning. Extensive simulation experiments are conducted in procedurally-generated environments to assess the performance contributions of this model and its individual components

Multicore and FPGA implementations of emotional-based agent architectures

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1307-6.Control architectures based on Emotions are becoming promising solutions for the implementation of future robotic agents. The basic controllers of the architecture are the emotional processes that decide which behaviors of the robot must activate to fulfill the objectives. The number of emotional processes increases (hundreds of millions/s) with the complexity level of the application, reducing the processing capacity of the main processor to solve complex problems (millions of decisions in a given instant). However, the potential parallelism of the emotional processes permits their execution in parallel on FPGAs or Multicores, thus enabling slack computing in the main processor to tackle more complex dynamic problems. In this paper, an emotional architecture for mobile robotic agents is presented. The workload of the emotional processes is evaluated. Then, the main processor is extended with FPGA co-processors through Ethernet link. The FPGAs will be in charge of the execution of the emotional processes in parallel. Different Stratix FPGAs are compared to analyze their suitability to cope with the proposed mobile robotic agent applications. The applications are set up taking into account different environmental conditions, robot dynamics and emotional states. Moreover, the applications are run also on Multicore processors to compare their performance in relation to the FPGAs. Experimental results show that Stratix IV FPGA increases the performance in about one order of magnitude over the main processor and solves all the considered problems. Quad-Core increases the performance in 3.64 times, allowing to tackle about 89 % of the considered problems. Quad-Core has a lower cost than a Stratix IV, so more adequate solution but not for the most complex application. Stratix III could be applied to solve problems with around the double of the requirements that the main processor could support. Finally, a Dual-Core provides slightly better performance than stratix III and it is relatively cheaper.This work was supported in part under Spanish Grant PAID/2012/325 of "Programa de Apoyo a la Investigacion y Desarrollo. Proyectos multidisciplinares", Universitat Politecnica de Valencia, Spain.Domínguez Montagud, CP.; Hassan Mohamed, H.; Crespo, A.; Albaladejo Meroño, J. (2015). Multicore and FPGA implementations of emotional-based agent architectures. Journal of Supercomputing. 71(2):479-507. https://doi.org/10.1007/s11227-014-1307-6S479507712Malfaz M, Salichs MA (2010) Using MUDs as an experimental platform for testing a decision making system for self-motivated autonomous agents. Artif Intell Simul Behav J 2(1):21–44Damiano L, Cañamero L (2010) Constructing emotions. Epistemological groundings and applications in robotics for a synthetic approach to emotions. In: Proceedings of international symposium on aI-inspired biology, The Society for the Study of Artificial Intelligence, pp 20–28Hawes N, Wyatt J, Sloman A (2009) Exploring design space for an integrated intelligent system. Knowl Based Syst 22(7):509–515Sloman A (2009) Some requirements for human-like robots: why the recent over-emphasis on embodiment has held up progress. Creat Brain Like Intell 2009:248–277Arkin RC, Ulam P, Wagner AR (2012) Moral decision-making in autonomous systems: enforcement, moral emotions, dignity, trust and deception. In: Proceedings of the IEEE, Mar 2012, vol 100, no 3, pp 571–589iRobot industrial robots website. http://www.irobot.com/gi/ground/ . Accessed 22 Sept 2014Moravec H (2009) Rise of the robots: the future of artificial intelligence. Scientific American, March 2009. http://www.scientificamerican.com/article/rise-of-the-robots/ . Accessed 14 Oct 2014.Thu Bui L, Abbass HA, Barlow M, Bender A (2012) Robustness against the decision-maker’s attitude to risk in problems with conflicting objectives. IEEE Trans Evolut Comput 16(1):1–19Pedrycz W, Song M (2011) Analytic hierarchy process (AHP) in group decision making and its optimization with an allocation of information granularity. IEEE Trans Fuzzy Syst 19(3):527–539Lee-Johnson CP, Carnegie DA (2010) Mobile robot navigation modulated by artificial emotions. IEEE Trans Syst Man Cybern Part B 40(2):469–480Daglarli E, Temeltas H, Yesiloglu M (2009) Behavioral task processing for cognitive robots using artificial emotions. Neurocomputing 72(13):2835–2844Ventura R, Pinto-Ferreira C (2009) Responding efficiently to relevant stimuli using an emotion-based agent architecture. Neurocomputing 72(13):2923–2930Arkin RC, Ulam P, Wagner AR (2012) Moral decision-making in autonomous systems: enforcement, moral emotions, dignity, trust and deception. Proc IEEE 100(3):571–589Salichs MA, Malfaz M (2012) A new approach to modeling emotions and their use on a decision-making system for artificial agents. Affect Comput IEEE Trans 3(1):56–68Altera Corporation (2011) Stratix III device handbook, vol 1–2, version 2.2. http://www.altera.com/literature/lit-stx3.jsp . Accessed 14 Oct 2014.Altera Corporation (2014) Stratix IV device handbook, vol 1–4, version 5.9. http://www.altera.com/literature/lit-stratix-iv.jsp . Accessed 14 Oct 2014.Naouar MW, Monmasson E, Naassani AA, Slama-Belkhodja I, Patin N (2007) FPGA-based current controllers for AC machine drives: a review. IEEE Trans Ind Electr 54(4):1907–1925Intel Corporation (2014) Desktop 4th generation Intel Core Processor Family, Desktop Intel Pentium Processor Family, and Desktop Intel Celeron Processor Family, Datasheet, vol 1, 2March JL, Sahuquillo J, Hassan H, Petit S, Duato J (2011) A new energy-aware dynamic task set partitioning algorithm for soft and hard embedded real-time systems. Comput J 54(8):1282–1294Del Campo I, Basterretxea K, Echanobe J, Bosque G, Doctor F (2012) A system-on-chip development of a neuro-fuzzy embedded agent for ambient-intelligence environments. IEEE Trans Syst Man Cybern Part B 42(2):501–512Pedraza C, Castillo J, Martínez JI, Huerta P, Bosque JL, Cano J (2011) Genetic algorithm for Boolean minimization in an FPGA cluster. J Supercomput 58(2):244–252Orlowska-Kowalska T, Kaminski M (2011) FPGA implementation of the multilayer neural network for the speed estimation of the two-mass drive system. IEEE Trans Ind Inf 7(3):436–445Cassidy AS, Merolla P, Arthur JV, Esser SK, Jackson B, Alvarez-icaza R, Datta P, Sawada J, Wong TM, Feldman V, Amir A, Ben-dayan D, Mcquinn E, Risk WP, Modha DS (2013) Cognitive computing building block: a versatile and efficient digital neuron model for neurosynaptic cores. 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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

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

CERA-CRANIUM: a test bed for machine consciousness research

Proceeding of: International Workshop on Machine Consciousness 2009. Hong Kong, China. 15-17. June, 2009.This paper describes a novel framework designed as a test bed for machine consciousness cognitive models (MCCM). This MCCM experimentation framework is based on a generalpurpose cognitive architecture that can be integrated in different environments and confronted with different problem domains. The definition of a generic cognitive control system for abstract agents is the root of the versatility of the presented framework. The proposed control system, which is inspired in the major cognitive theories of consciousness, provides mechanisms for both sensory data acquisition and motor action execution. Sensory and motor data is represented in the proposed architecture using different level workspaces where percepts and actions are generated thanks to the competition and collaboration of specialized processors. Additionally, this cognitive architecture provides the means to modulate perception and behavior; in other words, it offers an interface for a higher control layer to drive the way percepts and actions are generated and how they interact with each other. This mechanism permits the experimentation with virtually any high level cognitive model of consciousness. An illustrative application scenario, autonomous explorer robots, is also reviewed in this work.This research has been supported by the Spanish Ministry of Science and Innovation under CICYT grant TRA2007-67374-C02-02.No publicad

Sentience, the final frontier....

Arguments for fish sentience have difficulty with the philosophical zombie problem. Progress in AI has shown that complex learning, pain behavior, and pain as a motivational drive can be emulated by robots without any internal subjective experience. Therefore, demonstrating these abilities in fish does not necessarily demonstrate that fish are sentient. Further evidence for fish sentience may come from optogenetic studies of neural networks in zebrafish. Such studies may show that zebrafish have neural network patterns similar to those that correlate with sentience in humans. Given the present uncertainty regarding sentience in fish, caution should be applied regarding the precautionary principle. Adopting this principle may cause distress to humans, who are certainly sentient, as they strive to protect animals that may not be