A technique for simultaneous planning and action based on dynamic field theory is presented. The model builds on previous work on representation of sequential behavior as attractors in dynamic neural fields. Here, we demonstrate how chains of competing attractors can be used to represent dynamic plans towards a goal state. The present work can be seen as an addition to a growing body of work that demonstrates the role of dynamic field theory as a bridge between low-level reactive approaches and high-level symbol processing mechanisms. The architecture is evaluated on a set of planning problems using a simulated e-puck robot, including analysis of the system’s behavior in response to noise and temporary blockages of the planned route. The system makes no explicit distinction between planning and execution phases, allowing continuous adaptation of the planned path. The proposed architecture exploits the dynamic field theory property of stability in relation to noise and changes in the environment. The neural dynamics are also exploited such that stay-or-switch action selection emerges where blockage of a planned path occurs; stay until the transient blockage is removed versus switch to an alternative route to the goal.
AdamatzkyA.De Lacy CostelloB.MelhuishC.RatcliffeN. (2003). Experimental reaction–diffusion chemical processors for robot path planning. Journal of Intelligent and Robotic Systems, 37, 233–249.
2.
AmariS. (1977). Dynamics of pattern formation in lateral-inhibition type neural fields. Biological Cybernetics, 27(2), 77–87.
3.
ArbibM.A. (1985). Schemas for the temporal control of behavior. Human Neurobiology, 4(2), 63–72.
4.
ArkinR.C. (1998). Behaviour-Based Robotics. Cambridge, MA: MIT Press.
5.
BanquetJ.P.GaussierP.QuoyM.RevelA.BurnodY. (2002). Cortico-hippocampal maps and navigation strategies in robots and rodents. In Icsab proceedings of the seventh international conference on simulation of adaptive behavior on from animals to animats (pp. 141–150). Cambridge, MA: MIT Press.
6.
BartonM. J. (2001). Controller development and implementation for path planning and following in an autonomous urban vehicle. Phd thesis, The University of Sydney, Australia.
7.
BastianA.SchönerG.RiehleA. (2003). Preshaping and continuous evolution of motor cortical representations during movement preparation. European Journal of Neuroscience, 18(7), 2047–2058.
8.
BillingE. A.HellströmT. (2010). A formalism for learning from demonstration. Paladyn: Journal of Behavioral Robotics, 1(1): 1–13.
9.
BillingE.A.HellströmT.JanlertL.E. (2015). Simultaneous recognition and reproduction of demonstrated behavior. Biologically Inspired Cognitive Architectures, 12, 43–53.
10.
BrooksR.A. (1991). New approches to robotics. Science, 253, 1227–1232.
11.
BurgessN.RecceM.O’KeefeJ. (1994). A model of hippocampal function. Neural Networks, 7, 1065–1081.
12.
CisekP.KalaskaJ.F. (2010). Neural mechanisms for interacting with a world full of action choices. Annual Review of Neuroscience, 33(March), 269–298.
ErmentroutB. (1998). Neural networks as spatio-temporal pattern-forming systems. Reports on Progress in Physics, 61(4), 353–430.
15.
FyhnM.MoldenS.WitterM.P.MoserE.I.MoserM.-B. (2004). Spatial representation in the entorhinal cortex. Science (New York, N.Y.), 305(5688), 1258–1264.
16.
GaussierP.LeprêtreS.QuoiM.RevelA.JoulianC. (2000). Experiments and models about cognitive map learning and motivated navigation. In DemirisJ.BirkA. (Eds.), Interdisciplinary approaches to robot learning (pp. 53–94).
17.
GaussierP.RevelA.BanquetJ.P.BabeauV. (2002). From view cells and place cells to cognitive map learning: processing stages of the hippocampal system. Biological Cybernetics, 86(1), 15–28.
18.
GaussierP.ZrehenS. (1995). Perac: A neural architecture to control artificial animals. Robotics and Autonomous Systems, 16, 291–320.
19.
HaftingT.FyhnM.MoldenS.MoserM.-B.MoserE.I. (2005). Microstructure of a spatial map in the entorhinal cortex. Nature, 436(7052), 801–806.
20.
HirelJ.GaussierP.QuoyM.BanquetJ.P.SaveE.PoucetB. (2013). The hippocampo-cortical loop: spatio-temporal learning and goal-oriented planning in navigation. Neural Networks, 43, 8–21.
21.
HokV.SaveE.Lenck-SantiniP.P.PoucetB. (2005). Coding for spatial goals in the prelimbic/infralimbic area of the rat frontal cortex. Proceedings of the National Academy of Sciences of the United States of America, 102(12), 4602–4607.
22.
JefferyK.J.GilbertA.BurtonS.StrudwickA. (2003). Preserved performance in a hippocampal-dependent spatial task despite complete place cell remapping. Hippocampus, 13(2), 175–189.
23.
JohnsonJ.S.SpencerJ.P.SchönerG. (2008). Moving to higher ground: The dynamic field theory and the dynamics of visual cognition. New Ideas in Psychology, 26, 227–251.
24.
JohnsonJ.S.SpencerJ.P.SchönerG. (2009). A layered neural architecture for the consolidation, maintenance, and updating of representations in visual working memory. Brain Research, 1299, 17–32.
25.
KazerounianS.LuciwM.RichterM.SandamirskayaY. (2012). Autonomous reinforcement of behavioral sequences in neural dynamics. In Proceedings of the joint IEEE international conference on development and learning & epigenetic robotics (ICDL-EPIROB).
26.
KhatibO. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5, 90–98.
27.
KorenY.BorensteinJ. (1991). Potential field methods and their inherent limitations for mobile robot navigation. In IEEE international conference on robotics and automation (pp. 1398–1404). Sacramento, CA: IEEE.
28.
KulviciusT.TamosiunaiteM.AingeJ.DudchenkoP.WörgötterF. (2008). Odor supported place cell model and goal navigation in rodents. Journal of Computational Neuroscience, 25(3), 481–500.
29.
LaValleS.M. (2006). Planning Algorithms. Cambridge: Cambridge University Press. Retrieved from http://planning.cs.uiuc.edu/
30.
LuciwM.KazerounianS.LakhmannK.RichterM.SandamirskayaY. (2013). Learning the perceptual conditions of satisfaction of elementary behaviors. In Robotics: science and systems (RSS), workshop “active learning in robotics: exploration, curiosity, and interaction”.
31.
MartinetL.-E.PassotJ.-B.FouqueB.MeyerJ.-A.ArleoA. (2008). Map-based spatial navigation: a cortical column model for action planning. In FreksaC. (Ed.), Spatial cognition VI. Learning, reasoning, and talking about space. (pp. 39–55). Berlin: Springer.
32.
MatarićM. J. (1997). Behavior-based control: examples from navigation, learning, and group behavior. Journal of Experimental and Theoretical Artificial Intelligence, 9, 323–336.
33.
MatarićM. J. (2000). Sensory-motor primitives as a basis for imitation: linking perception to action and biology to robotics. In DautenhahnK.NehavinC. L. (Eds.), Imitation in animals and artifacts (pp.391–422). Cambridge, MA, USA: MIT Press.
34.
MilfordM.WyethG. (2008). Mapping a suburb with a single camera using a biologically inspired SLAM system. IEEE Transactions on Robotics, 24(5), 1038–1053.
35.
MondadaF.BonaniM.RaemyX.PughJ.CianciC.KlaptoczA.MartinoliA. (2009). The e-puck, a robot designed for education in engineering. In Proceedings of the 9th conference on autonomous robot systems and competitions (pp. 59–65).
36.
NicolescuM. (2003). A framework for learning from demonstration, generalization and practice in human-robot domains. PhD thesis, University of Southern California, USA.
37.
O’KeefeJ.NadelL. (1978). The Hippocampus as a Cognitive Map.Oxford: Oxford University Press.
38.
PoucetB.Lenck-SantiniP.P.HokV. (2004). Spatial navigation and hippocampal place cell firing - the problem of goal encoding. Reviews in the Neurosciences, 15, 89–107.
39.
Redisha.D.TouretzkyD.S. (1997). Cognitive maps beyond the hippocampus. Hippocampus, 7(1), 15–35.
40.
RichterM.SandamirskayaY.SchönerG. (2012). A robotic architecture for action selection and behavioral organization inspired by human cognition. In IEEE/RSJ international conference on intelligent robots and systems, IROS.
41.
RussellS.NorvigP. (1995). Artificial Intelligence: A Modern Approach (1st ed.). Englewood Cliffs, NJ: Prentice Hall.
42.
SandamirskayaY.RichterM.SchonerG. (2011). A neural-dynamic architecture for behavioral organization of an embodied agent. In IEEE International Conference on Development and Learning (ICDL), vol. 2, pp. 1–7.
43.
SandamirskayaY.SchönerG. (2010). An embodied account of serial order: How instabilities drive sequence generation. Neural Networks, 23(10), 1164–1179.
44.
SandamirskayaY.ZibnerS.SchneegansS.SchönerG. (2013). Using dynamic field theory to extend the embodiment stance toward higher cognition. New Ideas in Psychology, 31(3), 322–339.
45.
SargoliniF.FyhnM.HaftingT.McNaughtonB.L.WitterM.P.MoserM.-B.MoserE.I. (2006). Conjunctive representation of position, direction, and velocity in entorhinal cortex. Science (New York, N.Y.), 312(5774), 758–762.
SchönerG. (2008). Dynamical systems approaches to cognition. In Cambridge handbook of computational cognitive modeling (pp. 101–126). Cambridge, UK: Cambridge University Press.
48.
SchönerG.KopeczK.ErlhagenW. (1997). The dynamic neural field theory of motor programming: Arm and eye movements. Advances in Psychology, 119, 271–310.
49.
SiegwartR.NourbakhshI.ScaramuzzaD. (2011). Introduction to Autonomous Mobile Robots (2nd ed.). Cambridge, MA: The MIT Press.
50.
SpencerJ.P.PeroneS.JohnsonJ.S. (2009). Dynamic field theory and embodied cognitive dynamics. In SpencerJ.ThomasM.McClellandJ. (Eds.), Toward a unified theory of development: Connectionism and dynamic systems theory re-considered (pp. 86–118).New York: Oxford University Press.
51.
SpencerJ.P.SchönerG. (2003). Bridging the representational gap in the dynamical systems approach to development. Developmental Science, 6, 392–412.
52.
StrösslinT.SheynikhovichD.ChavarriagaR.GerstnerW. (2005). Robust self-localisation and navigation based on hippocampal place cells. Neural Networks, 18(9), 1125–1140.
53.
TaniJ.ItoM.SugitaY. (2004). Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB. Neural Networks, 17, 1273–1289.
54.
TaubeJ.S.MullerR.U.RanckJ.B. (1990a). Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. The Journal of Neuroscience, 10(2), 420–435.
55.
TaubeJ.S.MullerR.U.RanckJ.B. (1990b). Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. The Journal of Neuroscience, 10(2), 436–447.
56.
TolmanE.C. (1948). Cognitive maps in rats and men. The Psychological Review, 55(4), 189–208.
57.
TrevaiC.FukazawaY.YuasaH.OtaJ.AraiT.AsamaH. (2002). Cooperative exploration path planning for mobile robots by reaction–diffusion equation on graph. In IEEE international conference on industrial technology (ICIT) (Vol. 2, pp. 1266–1271).
58.
Vazquez-OteroA.FaiglJ.MuñuzuriA.P. (2012). Path planning based on reaction-diffusion process. In IEEE/RSJ international conference on intelligent robots and systems (pp. 896–901). Vilamoura, Algarve, Portugal.
59.
WilsonH.R.CowanJ.D. (1973). A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik, 13(2), 55–80.
60.
ZibnerS.K.U.FaubelC.IossifidisI.SchönerG. (2011). Dynamic neural fields as building blocks for a cortex-inspired architecture of robotic scene representation. IEEE Transactions on Autonomous Mental Development, 3(1), 74–91.
