Complete list of papers for "Motion Planning and Control Under Uncertainty"

Contact James Solberg if you need a copy of a paper

 

1. Abidi, B. Automatic sensor placement. in SPIE Conference on Intelligent Robots and Computer Vision XIV. 1995. Philadelphia, PA.

2. Allport, D., T.G. Zimmerman, and J.A. Paradiso, Electric field sensing and the "flying fish". 1995, MIT Media Laboratory - Physics and Media Group: Cambridge, Mass.

3. Ando, S., Intelligent three-dimensional vision sensor with ears. Sensors and Materials, 1995. 7(3): p. 213-231.

4. Andrade-Cetto, J. and A. Sanfeliu, The effects of partial observability when building fully correlated maps. Ieee Transactions on Robotics, 2005. 21(4): p. 771-777.

5. Arbel, T., On the sequential accumulation of evidence. International journal of computer vision, 2001. 43(3): p. 205-230.

6. Arulampalam, M.S., et al., A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 2002. 50(2): p. 174-188.

7. Assad, C., B. Rasnow, and P.K. Stoddard, Electric organ discharges and electric images during electrolocation. Journal of Experimental Biology, 1999. 202(10): p. 1185-1193.

8. Atick, J.J., Could Information-Theory Provide an Ecological Theory of Sensory Processing. Network-Computation in Neural Systems, 1992. 3(2): p. 213-251.

9. Bajcsy, R., Active perception. Proceedings of the IEEE, 1988. 76(8): p. 996-1005.

10. Barlow, H., Redundancy reduction revisited. Network-Computation in Neural Systems, 2001. 12(3): p. 241-253.

11. Barto, A., G., S.J. Bradtke, and S.P. Singh, Learning to Act using Real-Time Dynamic Programming. 1993, University of Massachusetts: Amherst, MA.

12. Barto, A.G., et al., A cerebellar model of timing and prediction in the control of reaching. Neural Comput, 1999. 11(3): p. 565-94.

13. Barto, A.G. and S. Mahadevan, Recent advances in hierarchical reinforcement learning. Discrete Event Dynamic Systems-Theory and Applications, 2003. 13(1-2): p. 41-77.

14. Bastian, J., Electrolocation .1. How the Electroreceptors of Apteronotus-Albifrons Code for Moving-Objects and Other Electrical Stimuli. Journal of Comparative Physiology, 1981. 144(4): p. 465-479.

15. Bastian, J., Plasticity of feedback inputs in the apteronotid electrosensory system. Journal of Experimental Biology, 1999. 202(10): p. 1327-1337.

16. Bastian, J. and W. Heiligenberg, Neural Correlates of the Jamming Avoidance-Response of Eigenmannia. Journal of Comparative Physiology, 1980. 136(2): p. 135-152.

17. Basye, K., et al., A Decision-Theoretic Approach to Planning, Perception, and Control. IEEE Expert-Intelligent Systems & Their Applications, 1992. 7(4): p. 58-65.

18. Bay, J.S. Tactile shape sensing via single- and multi-fingered hands. in IEEE international conference on robotics and automation. 1990. Scottsdale, AZ.

19. Bay, J.S., A Fully Autonomous Active Sensor-Based Exploration Concept for Shape-Sensing Robots. IEEE Transactions on Systems Man and Cybernetics, 1991. 21(4): p. 850-860.

20. Bay, J.S. and H. Hemami, Dynamics of a Learning Controller for Surface Tracking Robots on Unknown Surfaces. IEEE Transactions on Automatic Control, 1990. 35(9): p. 1051-1054.

21. Bell, C., et al., The generation and subtraction of sensory expectations within cerebellum-like structures. Brain Behavior and Evolution, 1997. 50: p. 17-31.

22. Bell, C.C., Memory-based expectations in electrosensory systems. Current Opinion in Neurobiology, 2001. 11(4): p. 481-487.

23. Benichou, O., et al., Optimal Search Strategies for Hidden Targets. Physical Review Letters, 2005. 94(19): p. 198101.

24. Bennett, A.T.D., Do animals have cognitive maps? Journal of Experimental Biology, 1996. 199(1): p. 219-224.

25. Berger, J.O., Statistical Decision Theory. 1980, Berlin: Springer-Verlag.

26. Berman, N.J. and L. Maler, Neural architecture of the electrosensory lateral line lobe: Adaptations for coincidence detection, a sensory searchlight and frequency-dependent adaptive filtering. Journal of Experimental Biology, 1999. 202(10): p. 1243-1253.

27. Bhushan, N. and R. Shadmehr, Computational nature of human adaptive control during learning of reaching movements in force fields. Biol Cybern, 1999. 81(1): p. 39-60.

28. Blake, R.W., Swimming in the Electric Eels and Knifefishes. Canadian Journal of Zoology-Revue Canadienne De Zoologie, 1983. 61(6): p. 1432-1441.

29. Blakemore, S.J., Deluding the motor system. Conscious Cogn, 2003. 12(4): p. 647-55.

30. Blakemore, S.J., D. Wolpert, and C. Frith, Why can't you tickle yourself? Neuroreport, 2000. 11(11): p. R11-6.

31. Blakemore, S.J., D.M. Wolpert, and C.D. Frith, Central cancellation of self-produced tickle sensation. Nat Neurosci, 1998. 1(7): p. 635-40.

32. Bleckmann, H., H. Schmitz, and G. von der Emde, Nature as a model for technical sensors. Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology, 2004. 190(12): p. 971-981.

33. Bonen, A., et al., A novel electrooptical proximity sensor for robotics: Calibration and active sensing. Ieee Transactions on Robotics and Automation, 1997. 13(3): p. 377-386.

34. Bonet, B. and H. Geffner. Planning with Incomplete Information as Heuristic Search in Belief Space. in 6th International Conf. on Artificial Intelligence Planning and Scheduling. 2000. Breckenridge, CO: AAAI Press.

35. Borst, A. and F.E. Theunissen, Information theory and neural coding. Nature Neuroscience, 1999. 2(11): p. 947-957.

36. Bourgault, F., et al. Information based adaptive robotic exploration. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002). 2002. Lausanne, Switzerland.

37. Boutilier, C., T. Dean, and S. Hanks, Decision-theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 1999. 11: p. 1-94.

38. Boutilier, C., et al. Decision-Theoretic, High-Level Agent Programming in the Situation Calculus. in Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence. 2000. Austin, TX: AAAI Press / The MIT Press.

39. Bovet, S. and R. Pfeifer. Emergence of delayed reward learning from sensorimotor coordinations. in IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005. Edmonton, Alberta, Canada.

40. Brammer, K. and G. Siffling, Kalman-Bucy Filters. 1989, Norwood, MA: Artech House, Inc.

41. Brandman, R. and M.E. Nelson, A simple model of long-term spike train regularization. Neural Computation, 2002. 14(7): p. 1575-1597.

42. Breder, C.M., The locomotion of fishes. Zoologica, 1926. 4: p. 159-297.

43. Bruyninckx, H. and J. de Schutter, The geometry of active sensing. 1999, Katholieke Universiteit Leuven, Dept. Mechanical Engineering: Leuven, Belguim.

44. Bryson, A.E. and Y.-C. Ho, Applied optimal control : optimization, estimation, and control. 1969, Waltham, Mass: Blaisdell Pub. Co. 481.

45. Bullock, T.H., Electroreception. Annual Review of Neuroscience, 1982. 5: p. 121-170.

46. Burgard, W., et al. Estimating the absolute position of a mobile robot using position probability grids. in Proc. of the Fourteenth National Conference on Artificial Intelligence. 1996. Portland, OR.

47. Burgard, W., D. Fox, and S. Thrun. Active mobile robot localization. in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI). 1997. San Mateo, CA: Morgan Kaufmann.

48. Burgard, W., D. Fox, and S. Thrun. Active mobile robot localization by entropy minimization. in Second Euromicro Workshop on Advanced Mobile Robots (EUROBOT '97). 1997. Brescia, ITALY.

49. Cain, P. and S. Malwal, Landmark use and development of navigation behaviour in the weakly electric fish Gnathonemus petersii (Mormyridae; Teleostei). Journal of Experimental Biology, 2002. 205(24): p. 3915-3923.

50. Callari, F., Active object recognition: Looking for differences. International journal of computer vision, 2001. 43(3): p. 189-204.

51. Callari, F.G. and F.P. Ferrie, Active Recognition: Using uncertainty to reduce ambiguity. 1995, Centre for Intelligent Machines; McGill University: Montreal, Quebec, Canada.

52. Carr, C.E., L. Maler, and E. Sas, Peripheral Organization and Central Projections of the Electrosensory Nerves in Gymnotiform Fish. Journal of Comparative Neurology, 1982. 211(2): p. 139-153.

53. Cassandra, A.R., Optimal Policies for Partially Observable Markov Decision Processes. 1994, Brown University, Department of Computer Science: Providence RI.

54. Cassandra, A.R., Exact and approximate algorithms for partially observable Markov decision processes, in Department of Computer Science. 1998, Brown University: Providence, RI. p. 474.

55. Cassandra, A.R., L.P. Kaelbling, and J.A. Kurien. Acting under uncertainty: Discrete Bayesian models for mobile robot navigation. in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 1996.

56. Cassandra, A.R., L.P. Kaelbling, and M.L. Littman. Acting optimally in partially observable stochastic domains. in Twelfth National Conference on Artificial Intelligence. 1994. Seattle, WA.

57. Cassandra, A.R., M.L. Littman, and N.L. Zhang. Incremental pruning: A simple, fast, exact algorithm for partially observable Markov decision processes. in Thirteenth Annual Conference on Uncertainty in Artificial Intelligence. 1997.

58. Castellanos, J., Multisensor fusion for simultaneous localization and map building. IEEE transactions on robotics and automation, 2001. 17(6): p. 908-914.

59. Chacron, M.J., A. Longtin, and L. Maler, Negative interspike interval correlations increase the neuronal capacity for encoding time-dependent stimuli. Journal of Neuroscience, 2001. 21(14): p. 5328-5343.

60. Chacron, M.J., et al., Suprathreshold stochastic firing dynamics with memory in P-type electroreceptors. Physical Review Letters, 2000. 85(7): p. 1576-1579.

61. Chacron, M.J., L. Maler, and J. Bastian, Electroreceptor neuron dynamics shape information transmission. Nature Neuroscience, 2005. 8(5): p. 673-678.

62. Chen, L., et al., Modeling signal and background components of electrosensory scenes. Journal of Comparative Physiology a-Neuroethology Sensory Neural and Behavioral Physiology, 2005. 191(4): p. 331-345.

63. Choset, H. and J. Burdick, Sensor-Based Exploration: The Hierarchical Generalized Voronoi Graph. The International Journal of Robotics Research, 2000. 19(2): p. 96-125.

64. Choset, H., et al., Principles of Robot Motion: Theory, Algorithms, and Implementations. 2005, Cambridge, MA: MIT Press. 603.

65. Choset, H. and K. Nagatani, Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization. Robotics and Automation, IEEE Transactions on, 2001. 17(2): p. 125-137.

66. Chrisman, L. Planning for closed-loop execution using partially observed Markovian decision processes. in AAAI Spring Symposium Series: Control of Selective Perception. 1992. Stanford University.

67. Chung, T.H., et al. On a decentralized active sensing strategy using mobile sensor platforms in a network. in Conference on Decision and Control. 2004.

68. Colgate, J.E. and K.M. Lynch, Mechanics and control of swimming: A review. IEEE Journal of Oceanic Engineering, 2004. 29(3): p. 660-673.

69. Collins, S., et al., Efficient bipedal robots based on passive-dynamic walkers. Science, 2005. 307(5712): p. 1082-1085.

70. Cover, T.M. and J.A. Thomas, Elements of Information Theory. Wiley Series in Telecommunications, ed. D.L. Schillings. 1991: John Wiley & Sons, Inc.

71. Davison, A.J., Modeling the world in real time: how robots engineer information. Philosophical Transactions of the Royal Society of London Series a-Mathematical Physical and Engineering Sciences, 2003. 361(1813): p. 2875-2890.

72. de Geeter, J., et al. Tolerance-weighted L-optimal experiment design for active sensing. in 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. 1998. Victoria, BC , Canada.

73. Dean, T. and K. Kanazawa, Persistence and Probabilistic Projection. IEEE Transactions on Systems Man and Cybernetics, 1989. 19(3): p. 574-585.

74. Dellaert, F., et al. Monte Carlo localization for mobile robots. in Proceedings of the IEEE International Conference on Robotics and Automation. 1999.

75. Dempster, A.P., N.M. Laird, and D.B. Rubin, Maximum Likelihood from Incomplete Data Via Em Algorithm. Journal of the Royal Statistical Society Series B-Methodological, 1977. 39(1): p. 1-38.

76. Denzler, J. and C.M. Brown, Information theoretic sensor data selection for active object recognition and, state estimation. Ieee Transactions on Pattern Analysis and Machine Intelligence, 2002. 24(2): p. 145-157.

77. Dissanayake, G., et al., Map management for efficient simultaneous localization and mapping (SLAM). Autonomous Robots, 2002. 12(3): p. 267-286.

78. Doucet, A., S. Godsill, and C. Andrieu, On sequential Monte Carlo sampling methods for Bayesian filtering, in Statistics and Computing. 2000. p. 197-208.

79. Doya, K., Complementary roles of basal ganglia and cerebellum in learning and motor control. Curr Opin Neurobiol, 2000. 10(6): p. 732-9.

80. Elfes, A., Using Occupancy Grids for Mobile Robot Perception and Navigation. Computer, 1989. 22(6): p. 46-57.

81. Feder, H.J.S., J.J. Leonard, and C.M. Smith, Adaptive mobile robot navigation and mapping. International Journal of Robotics Research, 1999. 18(7): p. 650-668.

82. Flash, T. and N. Hogan, The Coordination of Arm Movements - an Experimentally Confirmed Mathematical-Model. Journal of Neuroscience, 1985. 5(7): p. 1688-1703.

83. Fox, D., et al. Monte Carlo localization: efficient position estimation for mobile robots. in Proc. of the Sixteenth National Conference on Artificial Intelligence. 1999. Orlando, FL.

84. Fox, D., W. Burgard, and S. Thrun, Active Markov localization for mobile robots. Robotics and Autonomous Systems, 1998. 25(3-4): p. 195-207.

85. Fox, D., W. Burgard, and S. Thrun, Markov localization for mobile robots in dynamic environments. Journal of Artificial Intelligence Research, 1999. 11: p. 391-427.

86. Fox, D., et al., Bayesian filtering for location estimation. IEEE Pervasive Computing, 2003. 2(3): p. 24-33.

87. Fox, D., et al., Particle filters for mobile robot localization, in Sequential Monte Carlo Methods in Practice, A. Doucet, N. de Freitas, and N. Gordon, Editors. 2000, Springer-Verlag: New York.

88. Freeman, R.A., P. Yang, and K.M. Lynch. Distributed estimation and control of swarm formation statistics. in American Control Conference. 2006.

89. Gabbiani, F. and W. Metzner, Encoding and processing of sensory information in neuronal spike trains. Journal of Experimental Biology, 1999. 202(10): p. 1267-1279.

90. Gabbiani, F., et al., From stimulus encoding to feature extraction in weakly electric fish. Nature, 1996. 384(6609): p. 564-567.

91. Geffner, H., Modelling intelligent behaviour: The Markov decision process approach. Progress in Artificial Intelligence-Iberamia 98, 1998. 1484: p. 1-12.

92. Ghose, K., et al., Echolocating bats use a nearly time-optimal strategy to intercept prey. 2006, University of Maryland: College Park, MD.

93. Gonzalez, J.P. and A. Stentz. Planning with uncertainty in position: an optimal and efficient planner. in IEEE/RSJ International Conference on Intelligent Robots and Systems. 2005. Edmonton, Alberta, Canada.

94. Gonzalez-Banos, H., et al. Planning Robot Motion Strategies for Efficient Model Construction. in International Symposium on Robotics Research. 1999. Snowbird, UT.

95. Grewal, M.S. and A.P. Andrews, Chapter 4: Linear Optimal Filters, Predictors and Smoothers, in Kalman Filtering: Theory and Practice. 1993, Prentice-Hall, Inc.: Englewood Cliffs, NJ.

96. Grimson, W.E.L., Sensing Strategies for Disambiguating among Multiple Objects in Known Poses. IEEE Journal of Robotics and Automation, 1986. 2(4): p. 196-213.

97. Grocholosky, B., Information-theoretic control of multiple sensor platforms (PhD thesis), in Department of Aerospace, Mechatronic, and Mechanical Engineering. 2002, The University of Sydney: Sydney, NSW, Australia.

98. Grocholosky, B., H. Durrant-Whyte, and P. Gibbens. An information-theoretic approach to decentralized control of multiple autonomous flight vehicles. in Sensor Fusion and Decentralized Control in Robotic Systems III. 2000. Boston, MA, USA.

99. Hagiwara, S. and H. Morita, Coding Mechanisms of Electroreceptor Fibers in Some Electric Fish. Journal of Neurophysiology, 1963. 26(4): p. 551-&.

100. Hartmann, M.J., Active sensing capabilities of the rat whisker system. Autonomous Robots, 2001. 11(3): p. 249-254.

101. Hashemipour, H.R., S. Roy, and A.J. Laub, Decentralized Structures for Parallel Kalman Filtering. IEEE Transactions on Automatic Control, 1988. 33(1): p. 88-94.

102. Haykin, S., Bayesian estimation and Kalman filtering: neural implications. 2004: Hamilton, Ontario, Canada.

103. Healy, S., Spatial Representation in Animals. 1998, New York: Oxford University Press.

104. Heckerman, D., A tutorial on learning with bayesian networks. 1995, Microsoft Corperation: Redmond, WA.

105. Heiligenberg, W. and J. Bastian, The Electric Sense of Weakly Electric Fish. Annual Review of Physiology, 1984. 46: p. 561-583.

106. Heiligenberg, W. and J. Dye, Labeling of Electroreceptive Afferents in a Gymnotoid Fish by Intracellular Injection of Hrp - the Mystery of Multiple Maps. Journal of Comparative Physiology, 1982. 148(3): p. 287-296.

107. Hillis, J.M., et al., Combining sensory information: Mandatory fusion within, but not between, senses. Science, 2002. 298(5598): p. 1627-1630.

108. Holmes, P., J. Jenkins, and N.E. Leonard, Dynamics of the Kirchhoff equations I: Coincident centers of gravity and buoyancy. Physica D-Nonlinear Phenomena, 1998. 118(3-4): p. 311-342.

109. Hopkins, C.D., Stimulus Filtering and Electroreception - Tuberous Electroreceptors in 3 Species of Gymnotoid Fish. Journal of Comparative Physiology, 1976. 111(2): p. 171-207.

110. Horn, K.M., M. Pong, and A.R. Gibson, Discharge of inferior olive cells during reaching errors and perturbations. Brain Research, 2004. 996(2): p. 148-158.

111. Houk, J.C. and S.P. Wise, Distributed modular architectures linking basal ganglia, cerebellum, and cerebral cortex: their role in planning and controlling action. Cereb Cortex, 1995. 5(2): p. 95-110.

112. Hovland, G., Control of sensory perception in a mobile navigation problem. The International journal of robotics research, 1999. 18(2): p. 201-212.

113. Howard, A., M.J. Matari', and G. Sukhatme. Localization for Mobile Robot Teams Using Maximum Likelihood Estimation. in IEEE/RSJ International Conference on Robotics and Intelligent Systems. 2002. Lausanne, Switzerland.

114. Hu, X.M. and T. Ersson, Active state estimation of nonlinear systems. Automatica, 2004. 40(12): p. 2075-2082.

115. Hutchinson, S.A., R.L. Cromwell, and A.C. Kak. Planning sensing strategies in a robot work cell with multi-sensor capabilities. in IEEE International Conference on Robotics and Automation. 1988. Philadelphia, PA, USA.

116. Isard, M. and A. Blake, CONDENSATION - Conditional density propagation for visual tracking. International Journal of Computer Vision, 1998. 29(1): p. 5-28.

117. Jensfelt, P. and S. Kristensen, Active global localization for a mobile robot using multiple hypothesis tracking. IEEE Transactions on Robotics and Automation, 2001. 17(5): p. 748-760.

118. Julier, S., J. Uhlmann, and H.F. Durrant-Whyte, A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 2000. 45(3): p. 477-482.

119. Kaelbling, L.P., A.R. Cassandra, and J.A. Kurien. Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation. in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 1996.

120. Kaelbling, L.P., M.L. Littman, and A.R. Cassandra, Planning and acting in partially observable stochastic domains. Artificial Intelligence, 1998. 101(1-2): p. 99-134.

121. Kakei, S., D.S. Hoffman, and P.L. Strick, Sensorimotor transformations in cortical motor areas. Neurosci Res, 2003. 46(1): p. 1-10.

122. Kalandros, M. and L.Y. Pao. Controlling target estimate covariance in centralized multisensor systems. in American Control Conference. 1998. Philadelphia, PA.

123. Kallenberg, L., Markov Decision Processes. 2000, Leiden University, The Netherlands.

124. Kalman, R.E., A new approach to linear filtering and prediction problems. Transactions of the ASME - Journal of Basic Engineering, 1960. 82: p. 35-45.

125. Kane, B.J., M.R. Cutkosky, and G.T.A. Kovacs, A traction stress sensor array for use in high-resolution robotic tactile imaging. Journal of Microelectromechanical Systems, 2000. 9(4): p. 425-434.

126. Kaneko, M., N. Kanayama, and T. Tsuji, Vision-based active sensor using a flexible beam. IEEE-ASME Transactions on Mechatronics, 2001. 6(1): p. 7-16.

127. Kaneko, M., H. Maekawa, and K. Tanie. Active tactile sensing by robotic fingers based on minimum-external-sensor-realization. in IEEE International Conference on Robotics and Automation. 1992. Nice, France.

128. Knill, D.C. and A. Pouget, The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in Neurosciences, 2004. 27(12): p. 712-719.

129. Koening, S. and R.G. Simmons, Xavier: A robot navigation architecture based on partially observable Markov decision process models, in Artificial Intelligence and Mobile Robots, D. Kortenkamp, R.P. Bonasso, and R. Murphy, Editors. 1998, American Association for Artificial Intelligence: Menlo Park, CA.

130. Konig, P. and H. Luksch, Active sensing - Closing multiple loops. Zeitschrift Fur Naturforschung C-a Journal of Biosciences, 1998. 53(7-8): p. 542-549.

131. Kording, K.P., et al., A neuroeconomics approach to inferring utility functions in sensorimotor control. Plos Biology, 2004. 2(10): p. 1652-1656.

132. Kording, K.P., S.P. Ku, and D.M. Wolpert, Bayesian integration in force estimation. Journal of Neurophysiology, 2004. 92(5): p. 3161-3165.

133. Kording, K.P. and D.M. Wolpert, Bayesian integration in sensorimotor learning. Nature, 2004. 427(6971): p. 244-247.

134. Kording, K.P. and D.M. Wolpert, The loss function of sensorimotor learning. Proceedings of the National Academy of Sciences of the United States of America, 2004. 101(26): p. 9839-9842.

135. Krahe, R., et al., Stimulus encoding and feature extraction by multiple sensory neurons. Journal of Neuroscience, 2002. 22(6): p. 2374-2382.

136. Kreucher, C., K. Kastella, and A.O. Hero, Multi-target sensor management using alpha-divergence measures. Information Processing in Sensor Networks, Proceedings, 2003. 2634: p. 209-222.

137. Kreucher, C., K. Kastella, and A.O. Hero, Sensor management using an active sensing approach. Signal Processing, 2005. 85(3): p. 607-624.

138. Kristensen, S., Sensor planning with Bayesian decision theory. Robotics and autonomous systems, 1997. 19(3-4): p. 273-286.

139. Kuipers, B. and Y.-T. Byun, A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations. Journal of Robotics and Autonomous Systems, 1991. 8: p. 47-63.

140. Kuo, A.D., The relative roles of feedforward and feedback in the control of rhythmic movements. Motor Control, 2002. 6(2): p. 129-145.

141. Lamb, H., Hydrodynamics. 6th ed. 1932, New York: Dover.

142. Lan, N., Analysis of an optimal control model of multi-joint arm movements. Biological Cybernetics, 1997. 76(2): p. 107-117.

143. Lannoo, M.J. and S.J. Lannoo, Why Do Electric Fishes Swim Backwards - an Hypothesis Based on Gymnotiform Foraging Behavior Interpreted through Sensory Constraints. Environmental Biology of Fishes, 1993. 36(2): p. 157-165.

144. Laporte, C., R. Brooks, and T. Arbel. A fast discriminant approach to active object recognition and pose estimation. in Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on. 2004.

145. LaValle, S.M., Planning Algorithms. 2006, http://msl.cs.uiuc.edu/planning/: Cambridge University Press.

146. LaValle, S.M. and S.A. Hutchinson, An objective-based framework for motion planning under sensing and control uncertainties. International Journal of Robotics Research, 1998. 17(1): p. 19-42.

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151. Lefebvre, T., H. Bruyninckx, and J. De Schutter, Task planning with active sensing for autonomous compliant motion. International Journal of Robotics Research, 2005. 24(1): p. 61-81.

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153. Leonard, J.J., H.F. Durrant-Whyte, and I.J. Cox, Dynamic Map Building for an Autonomous Mobile Robot. International Journal of Robotics Research, 1992. 11(4): p. 286-298.

154. Leonard, J.J., et al., Mapping partially observable features from multiple uncertain vantage points. International Journal of Robotics Research, 2002. 21(10-11): p. 943-975.

155. Lewis, J.E. and L. Maler, Neuronal population codes and the perception of object distance in weakly electric fish. Journal of Neuroscience, 2001. 21(8): p. 2842-2850.

156. Li, X.Y. and Q. Ji, Active affective state detection and user assistance with dynamic Bayesian networks. IEEE Transactions on Systems Man and Cybernetics Part a-Systems and Humans, 2005. 35(1): p. 93-105.

157. Lindner, B., M.J. Chacron, and A. Longtin, Integrate-and-fire neurons with threshold noise: A tractable model of how interspike interval correlations affect neuronal signal transmission. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics), 2005. 72(2): p. 021911.

158. Linsker, R., Perceptual Neural Organization - Some Approaches Based on Network Models and Information-Theory. Annual Review of Neuroscience, 1990. 13: p. 257-281.

159. Lissmann, H.W., On the Function and Evolution of Electric Organs in Fish. Journal of Experimental Biology, 1958. 35(1): p. 156-&.

160. Lissmann, H.W. and K.E. Machin, The Mechanism of Object Location in Gymnarchus-Niloticus and Similar Fish. Journal of Experimental Biology, 1958. 35(2): p. 451-486.

161. Littman, M.L., Algorithms for Sequential Decision Making, in Department of Computer Science. 1996, Brown University: Providence, RI.

162. Liu, S. and L.E. Holloway, Active sensing policies for stochastic systems. Ieee Transactions on Automatic Control, 2002. 47(2): p. 373-377.

163. Lorussi, F., A. Marigo, and A. Bicchi. Optimal exploratory paths for a mobile rover. in International Conference on Robotics and Automation. 2001. Seoul, Korea.

164. Lovejoy, W.S., Computationally Feasible Bounds for Partially Observed Markov Decision-Processes. Operations Research, 1991. 39(1): p. 162-175.

165. Lumelsky, V.J. and T. Skewis, Incorporating range sensing in the robot navigation function. IEEE Transactions on Systems, Man and Cybernetics, 1990. 20(5): p. 1058-1069.

166. Lygeros, J., C. Tomlin, and S. Sastry, Controllers for reachability specifications for hybrid systems. Automatica, 1999. 35(3): p. 349-370.

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