Embodied Cognitive Robotics focuses its attention on the design of artificial agents capable of performing cognitive tasks autonomously. A central issue in this consists in studying process by which agents learn through interaction with their environment. Embodied Cognitive Robotics aims to implement models of cognitive processes coming from Cognitive Sciences. The guidelines in this research area are a direct response to the shortcomings of Classical Artificial Intelligence, where high-level tasks and behaviors were studied. This article describes the work carried out in the Cognitive Robotics Laboratory at the Universidad Autónoma del Estado de Morelos (UAEM). Our work is based on the concept of low-level sensorimotor schemes coded by Internal Models, thus falling as a matter of course within the tenets of Embodied Cognition, particularly with the idea that cognition must be understood as occurring in agents that have a body with which they interact in a specific environment. It is through this interaction that learning emerges laying the ground for cognitive processes. Our research includes theoretical work laying the foundations of Embodied Cognitive Robotics, as well as work with artificial and with natural agents.
AdamsR. A.ShippS.FristonK. J. (2013). Predictions not commands: Active inference in the motor system. Brain Structure & Function, 218, 611–643.
2.
AdolphK.BergerS. (2006). Motor development. In DamonW.LernerR.KuhnD.SieglerR. S. (Eds.), Handbook of child psychology: Cognition, perception, and language (vol. 2, pp. 161–213, 6th ed.). New York, NY: John Wiley & Sons.
3.
AndersonM. L. (2003). Embodied cognition: A field guide. Artificial Intelligence, 149, 91–130.
4.
ArceoD. C.EscobarE.HermosilloJ.LaraB. (2013). Modelado de un sistema de neuronas espejo en un agente autónomo artificial model of a mirror neuron system in an artificial autonomous agent. Nova Scientia, 5, 51–72.
5.
AsadaM.HosodaK.KuniyoshiY.IshiguroH.InuiT.YoshikawaY.YoshidaC. (2009). Cognitive developmental robotics: A survey. IEEE Transactions on Autonomous Mental Development, 1, 12–34.
6.
AstorgaD. (2016) Efecto de Vinculación Temporal: un efecto dependiente de la agencia o de la contigüidad y predictibilidad de los eventos? (Master’s thesis). Centro de Investigación en Ciencias Cognitivas, Cuernavaca, Mexico.
7.
BarsalouL. W. (2008). Grounded cognition. Annual Review of Psychology, 59, 617–645.
8.
BlakemoreS. J.FrithC. D.WolpertD. M. (1999). Spatio-temporal prediction modulates the perception of self-produced stimuli. Journal of Cognitive Neuroscience, 11, 551–559.
9.
BlakemoreS. J.SmithJ.SteelR.JohnstoneE.FrithC. D. (2000a). The perception of self-produced sensory stimuli in patients with auditory hallucinations and passivity experiences: Evidence for a breakdown in self-monitoring. Psychological Medicine, 30, 1131–1139.
10.
BlakemoreS. J.WolpertD.FrithC. (2000b). Why can’t you tickle yourself?Neuroreport, 11, R11–R16.
11.
BrooksR. A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.
12.
BucknerC.FridlandE. (2017). What is cognition? Angsty monism, permissive pluralism(s), and the future of cognitive science. Synthese, 194, 4191–4195.
13.
CasileA.GieseM. A. (2006). Nonvisual motor training influences biological motion perception. Current Biology, 16, 69–74.
14.
ChielH. J.BeerR. D. (1997). The brain has a body: Adaptive behavior emerges from interactions of nervous system, body and environment. Trends in Neurosciences, 20, 553–557.
15.
ClarkA. (1999). An embodied cognitive science?Trends in Cognitive Sciences, 3, 345–351.
16.
ClarkA. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36, 181–204.
17.
ClarkA. (2015a). Embodied prediction. In: MetzingerT.WindtJ. M. (Eds.). Open mind (pp 1–21). Frankfurt Am Main, Germany: Mind Group.
18.
ClarkA. (2015b). Surfing uncertainty: Prediction, action, and the embodied mind. Oxford, UK: Oxford University Press.
19.
ClarkA.GrushR. (1999). Towards a cognitive robotics. Adaptive Behavior, 7, 5–16.
20.
DemirisJ.HayesG. M. (2002). Imitation in animals and artifacts. In NehanivC. L.DautenhahnK. (Eds.), Imitation as a dual-route process featuring predictive and learning components: A biologically plausible computational model (pp. 327–361). Cambridge, MA: The MIT Press.
21.
DemirisY.DeardenA. (2005). From motor babbling to hierarchical learning by imitation: A robot developmental pathway. Retrieved from http://cogprints.org/4961/1/demiris.pdf
22.
DesmurgetM.GraftonS. (2000). Forward modeling allows feedback control for fast reaching movements. Trends in Cognitive Sciences, 4, 423–431.
23.
Di PaoloE. (2009). Extended life. Topoi, 28(1), 9–21.
24.
DreyfusH. L. (1967). Why computers must have bodies in order to be intelligent. The Review of Metaphysics, 21(1), 13–32.
25.
EscobarE.HermosilloJ.LaraB. (2012). Self body mapping in mobile robots using vision and forward models. In Proceedings of the 9th IEEE Electronics, Robotics and Automotive Mechanics Conference (CERMA) (pp. 72–77). New York, NY: IEEE Computer Society.
26.
Escobar JuárezE. (2016) Modelado de una arquitectura de modelos internos para la generación de esquemas sensorimotrices (PhD thesis). Instituto de Investigación en Ciencias Básicas y Aplicadas, Cuernavaca, Mexico.
27.
Escobar JuárezE.SchillaciG.Hermosillo ValadezJ.Lara GuzmánB. (2016). A self-organized internal models architecture for coding sensory–motor schemes. Frontiers in Robotics and AI, 3, Article 22.
28.
FristonK.KilnerJ.HarrisonL. (2006). A free energy principle for the brain. Journal of Physiology-Paris, 100, 70–87.
29.
FristonK. J.DaunizeauJ.KilnerJ.KiebelS. J. (2010). Action and behavior: A free-energy formulation. Biological Cybernetics, 102, 227–260.
30.
FrithC. D.BlakemoreS. J.WolpertD. M. (2000). Explaining the symptoms of schizophrenia: Abnormalities in the awareness of action. Brain Research Reviews, 31, 357–363.
31.
GaonaW. (2014) Percepción de Distancia en un Agente Artificial a través de la Simulación de Esquemas Sensorimotrices (PhD thesis). Facultad de Ciencias, Montevideo, Uruguay.
32.
GaonaW.EscobarE.HermosilloJ.LaraB. (2015). Anticipation by multi-modal association through an artificial mental imagery process. Connection Science, 27, 68–88.
33.
GaonaW.HermosilloJ.LaraB. (2012). Distance perception in mobile robots as an emergent consequence of visuo-motor cycles using forward models. In Proceedings of the IEEE 9th Electronics, Robotics and Automotive Mechanics Conference (pp. 42–47). New York, NY: IEEE Computer Society.
34.
HaggardP.ChambonV. (2012). Sense of agency. Current Biology, 22, R390–R392.
HaggardP.TsakirisM. (2009). The experience of agency: Feelings, judgments, and responsibility. Current Directions in Psychological Science, 18, 242–246.
37.
HarveyK. (2018). An open-ended approach to Piagetian development of adaptive behavior. Open Access Library Journal, 5, e4434.
38.
Hernandez OchoaJ. L. (2017) Robotica Cognitiva y Emociones (Master’s thesis). Centro de Investigación en Ciencias Cognitivas, Cuernavaca, Mexico.
39.
HeyesC. (2010). Where do mirror neurons come from?Neuroscience and Biobehavioral Reviews, 34, 575–583.
40.
HoffmannH. (2007). Perception through visuomotor anticipation in a mobile robot. Neural Networks, 20, 22–33.
41.
InceG.NakadaiK.RodemannT.HasegawaY.TsujinoH.ImuraJ. I. (2009). Ego noise suppression of a robot using template subtraction. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 199–204). New York, NY: IEEE.
42.
InceG.NakadaiK.RodemannT.HasegawaY.TsujinoH.ImuraJ. I. (2010). A hybrid framework for ego noise cancellation of a robot. In XiN.HamelW. R. (Eds.), Proceedings of the IEEE International Conference on Robotics and Automation (pp. 3623–3628). New York, NY: IEEE.
43.
JordanM. I.RumelhartD. E. (1992). Forward models: Supervised learning with a distal teacher. Cognitive Science, 16, 307–354.
44.
KarnielA. (2002). Three creatures named “forward model.” Neural Networks, 15, 305–307.
45.
KawatoM. (1999). Internal models for motor control and trajectory planning. Current Opinion in Neurobiology, 9, 718–727.
46.
KiversteinJ. (2007). Could a robot have a subjective point of view?Journal of Consciousness Studies, 14, 127–139.
47.
KiversteinJ.ClarkA. (2009). Introduction: Mind embodied, embedded, enacted: One church or many?Topoi, 28, 1–7.
48.
KnoblichG.FlachR. (2001). Predicting the effects of actions: Interactions of perception and action. Psychological Science, 12, 467–472.
49.
KnoblichG.SeigerschmidtE.FlachR.PrinzW. (2002). Authorship effects in the prediction of handwriting strokes: Evidence for action simulation during action perception. The Quarterly Journal of Experimental Psychology: Section A, 55, 1027–1046.
50.
KoeinigP.KtihnbergerK. U.KietzmannT. C. (2013). A unifying approach to high-and low-level cognition. In GähdeU. (Ed.), Models, simulations, and the reduction of complexity (vol. 4, pp. 117–139). Berlin, Germany: De Gruyter.
51.
KohonenT. (1998). The self-organizing map. Neurocomputing, 21, 1–6.
52.
LacquanitiF.IvanenkoY. P.ZagoM. (2012). Development of human locomotion. Current Opinion in Neurobiology, 22, 822–828.
53.
LakoffG.JohnsonM. (1999). Philosophy in the Flesh (vol. 4). New York, NY: Basic books.
54.
LaraB.CiriaA.EscobarE.GaonaW.HermosilloJ. (2018). Cognitive robotics: The new challenges in artificial intelligence. In VillegasO. O. S.NandayapaM.SotoI. (Eds.), Advanced topics on computer vision, control and robotics in Mechatronics (pp. 321–348). Berlin, Germany: Springer International Publishing AG.
55.
Lara GuzmánB.Hermosillo ValadezJ.Baeza MariscalK. (2017). Intentionality and internal models in artificial agents. Pragmatics & Cognition, 23, 209–237.
56.
LüchingerA. B.Hadders-AlgraM.Van KanC. M.de VriesJ. I. (2008). Fetal onset of general movements. Pediatric Research, 63, 191–195.
57.
McCarthyJ.MinskyM. L.RochesterN.ShannonC. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine, 27, 12.
58.
MayeA.EngelA. K. (2012). Using sensorimotor contingencies for prediction and action planning. In ZiemkeT.BalkeniusC.HallamJ. (Eds.), Proceedings of the International Conference on Simulation of Adaptive Behavior (pp. 106–116). Berlin, Germany: Springer.
59.
MenaryR. (2010). Introduction to the special issue on 4E cognition. Phenomenology and the Cognitive Sciences, 9, 459–463.
60.
Mendoza BockE. (2013) Los términos nómadas de las Ciencias Cognitivas: el caso de los Modelos Directos (Master’s thesis). Facultad de Humanidades, Montevideo, Uruguay.
61.
MiallR. C. (2003). Connecting mirror neurons and forward models. Neuroreport, 14, 2135–2137.
62.
MiallR. C.KingD. (2008). State estimation in the cerebellum. The Cerebellum, 7, 572–576.
63.
MiallR. C.WolpertD. M. (1996). Forward models for physiological motor control. Neural Networks, 9, 1265–1279.
64.
MiallR.WeirD. J.WolpertD. M.SteinJ. (1993). Is the cerebellum a smith predictor?Journal of Motor Behavior, 25, 203–216.
65.
MingersJ. (2001). Embodying information systems: The contribution of phenomenology. Information and Organization, 11, 103–128.
66.
MöllerR.SchenckW. (2008). Bootstrapping cognition from behavior a computerized thought experiment. Cognitive Science, 32, 504–542.
67.
MoonsT. (1998). Lecture notes in computer science. In KochR.GoolL. V. (Eds.), 3D structure from multiple images of large-scale environments (Chapter: A guided tour through multi view relations) (vol. 1506, pp. 304–346). Berlin, Germany: Springer.
68.
MoravecH. (1984) Locomotion, vision and intelligence. In BradyM.PaulR. (Eds.), Robotics Research (pp. 215–224). Cambridge, MA: The MIT Press.
NewellA.SimonH. A. (1976). Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19, 113–126.
73.
NewenA. (2017). What are cognitive processes? An example-based approach. Synthese, 194, 4251–4268.
74.
NoëA. (2004). Action in perception. Cambridge, MA: The MIT Press.
75.
OztopE.KawatoM.ArbibM. (2006). Mirror neurons and imitation: A computationally guided review. Neural Networks, 19, 254–271. doi:10.1016/j.neunet.2006.02.002.
76.
PezzuloG.BarsalouL. W.CangelosiA.FischerM. H.McRaeK.SpiveyM. J. (2011). The mechanics of embodiment: A dialog on embodiment and computational modeling. Frontiers in Psychology, 2, 5.
77.
PfeiferR.BongardJ. (2006). How the body shapes the way we think: A new view of intelligence. Cambridge, MA: The MIT Press.
PfeiferR.ScheierC. (2001). Understanding intelligence. Cambridge, MA: The MIT Press.
80.
PickeringM. J.ClarkA. (2014). Getting ahead: Forward models and their place in cognitive architecture. Trends in Cognitive Sciences, 18, 451–456.
81.
PicoA.SchillaciG.HafnerV. V.LaraB. (2017). How do I sound like? Forward models for robot ego-noise prediction. In Santos - VictorJ.SandiniG., (Eds.), Proceedings of the Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (pp. 246–251). New York, NY: IEEE.
82.
RiedmillerM.BraunH. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In Proceedings of the IEEE International Conference on Neural Networks (pp. 586–591). New York, NY: IEEE.
83.
RobbinsP.AydedeM. (2009). The Cambridge handbook of situated cognition. Cambridge, UK: Cambridge University Press.
84.
RodríguezD. (2012) Análisis de la Cognición Corporizada desde la perspectiva de la fenomenología (Master’s thesis). Facultad de Humanidades, Montevideo, Uruguay.
85.
RodríguezD.HermosilloJ.LaraB. (2012). Meaning in artificial agents: The symbol grounding problem revisited. Minds and Machines, 22, 25–34.
86.
RojasE. (2014) Estudio sobre la caracterización de acciones basado en modelos internos (Master’s thesis). Facultad de Humanidades, Montevideo, Uruguay.
RowlandsM. (2010). The new science of the mind: From extended mind to embodied phenomenology. Cambridge, MA: The MIT Press.
89.
RumelhartD. E.HintonG. E.WilliamsR. J. (1985). Learning internal representations by error propagation (Technical Report). California Univ San Diego La Jolla Inst for Cognitive Science. Retrieved from http://www.dtic.mil/dtic/tr/fulltext/u2/a164453.pdf
90.
SchillaciG.HafnerV. V.LaraB. (2012a). Coupled inverse-forward models for action execution leading to tool-use in a humanoid robot. In HanJ.TapusA.CrandallJ. (Eds.), Proceedings of the 7th ACM/IEEE International Conference on Human-Robot Interaction (pp. 231–232). New York, NY: IEEE.
91.
SchillaciG.HafnerV. V.LaraB. (2016). Exploration behaviors, body representations, and simulation processes for the development of cognition in artificial agents. Frontiers in Robotics and AI, 3, 39.
92.
SchillaciG.HafnerV. V.LaraB.GrosjeanM. (2013). Is that me? Sensorimotor learning and self-other distinction in robotics. In CrandallJ.SabanovicS., (Eds.), Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction (pp. 223–224). New York, NY: IEEE.
93.
SchillaciG.LaraB.HafnerV. (2012b). Internal simulations for behaviour selection and recognition. In SalahA.Ruiz-del SolarJ.MericliC.OudeyerP. Y. (Eds.), Human behavior understanding (Lecture notes in computer science) (vol. 7559, pp. 148–160). Berlin, Germany: Springer.
94.
SchmidtA.DeleforgeA.KellermannW.SchmidtA.DeleforgeA.KellermannW.KellermannW. (2016). Ego-noise reduction using a motor data-guided multichannel dictionary. In BurgardW. (Ed.) Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1281–1286). New York, NY: IEEE.
95.
ShapiroL. (2007). The embodied cognition research programme. Philosophy Compass, 2, 338–346.
ShapiroL. A. (2004). The mind incarnate. Cambridge, MA: The MIT Press.
98.
TsaiR. Y. (1987). A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. Journal of Robotics and Automation, 3, 323–344.
99.
TsakirisM.Schütz-BosbachS.GallagherS. (2007). On agency and body-ownership: Phenomenological and neurocognitive reflections. Consciousness and Cognition, 16, 645–660.
100.
Van GelderT.PortR. F. (1995). It’s about time: An overview of the dynamical approach to cognition. In PortR.van GelderT. (Eds.), Mind as motion: Explorations in the dynamics of cognition (pp. 1–43). Cambridge, MA: The MIT Press.
101.
VarelaF. J.ThompsonE.RoschE. (2017). The embodied mind: Cognitive science and human experience. Cambridge, MA: The MIT press.
102.
von HofstenC. (2003). On the development of perception and action. London, England: SAGE.
103.
von HofstenC. (2007). Action in development. Developmental Science, 10, 54–60.
104.
von HofstenC. (2009). Action, the foundation for cognitive development. Scandinavian Journal of Psychology, 50, 617–623.
105.
WardD.StapletonM. (2012). Es are good. In PaglieriF. (Ed.), Consciousness in interaction: The role of the natural and social context in shaping consciousness (pp. 86–89). Amsterdam, The Netherlands: John Benjamins Publishing.
106.
WebbB. (2004). Neural mechanisms for prediction: Do insects have forward models?Trends in Neurosciences, 27, 278–282.
107.
WegnerD. M.WheatleyT. (1999). Apparent mental causation: Sources of the experience of will. American Psychologist, 54, 480–492.
108.
WeissC.TsakirisM.HaggardP.Schütz-BosbachS. (2014). Agency in the sensorimotor system and its relation to explicit action awareness. Neuropsychologia, 52, 82–92.
109.
WheelerM.ClarkA. (2008). Culture, embodiment and genes: Unravelling the triple helix. Philosophical Transactions of the Royal Society B: Biological Sciences, 363, 3563–3575.
110.
WilsonM. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9, 625–636.
111.
WilsonM.KnoblichG. (2005). The case for motor involvement in perceiving conspecifics. Psychological Bulletin, 131, 460–473.
112.
WolpertD. M.GhahramaniZ.FlanaganJ. R. (2001). Perspectives and problems in motor learning. Trends in Cognitive Sciences, 5, 487–494.
113.
WolpertD. M.GhahramaniZ.JordanM. I. (1995). An internal model for sensorimotor integration. Science, 269, 1880–1882.
114.
WolpertD. M.GoodbodyS. J.HusainM. (1998a). Maintaining internal representations: The role of the human superior parietal lobe. Nature Neuroscience, 1, 529–533.
115.
WolpertD. M.MiallR. C.KawatoM. (1998b). Internal models in the cerebellum. Trends in Cognitive Sciences, 2, 338–347.
116.
ZambelliM.DemirisY. (2015). Online ensemble learning of sensorimotor contingencies. In TéllezR.AlenyàG.AnguloC.KevinO’Regan, (Eds.) Proceedings of the Sensorimotor Contingencies for Robotics Workshop at IROS. Berlin, Germany: Springer.
