This paper describes Minerva, an interactive tour-guide robot that was successfully deployed in a Smithsonian museum. Minerva’s software is pervasively probabilistic, relying on explicit representations of uncertainty in perception and control. During 2 weeks of operation, the robot interacted with thousands of people, both in the museum and through the Web, traversing more than 44 km at speeds of up to 163 cm/sec in the unmodified museum.
Get full access to this article
View all access options for this article.
References
1.
Arkin, R. 1998. Behavior-Based Robotics. Boston, MA: MIT Press .
2.
Arras, K. O., and Vestli, S. J. 1998. Hybrid, high-precision localization for the mail distributing mobile robot system MOPS . Proceedings of the IEEE International Conference on Robotics & Automation (ICRA), Leuven, Belgium.
3.
Asoh, H., Hayamizu, S., Isao, H., Motomura, Y., Akaho, S., and Matsui, T. 1997. Socially embedded learning of office-conversant robot jijo-2 . Proceedings of IJCAI-97, Nagoya, Japan, pp. 880–888 .
4.
Bates, J. 1994 (April). The role of emotion in believable agents. Technical Report CMU-CS-94-136, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA .
5.
Beetz, M. 1999. Structured reactive controllers—A computational model of everyday activity . Proceedings of the Third International Conference on Autonomous Agents.
6.
Beetz, M., Bennewitz, M., and Grosskreutz, H. 1999. Probabilistic, prediction-based schedule debugging for autonomous robot office couriers . Proceedings of the 23rd German Conference on Artificial Intelligence (KI 99), Bonn, Germany.
7.
Beetz, M., Burgard, W., Fox, D., and Cremers, A. B. 1999. Integrating active localization into high-level robot control systems . Journal of Robotics and Autonomous Systems. Forthcoming.
8.
Beetz, M., and Grosskreutz, H. 2000. Probabilistic hybrid action models for predicting concurrent percept-driven robot behavior . Proceedings of the Fifth International Conference on AI Planning Systems, Breckenridge, CO, 2000.
9.
Bellman, R. E. 1957. Dynamic Programming. Princeton, NJ: Princeton University Press .
10.
Betke, M., and Gurvits, L. 1993 (December). Mobile robot localization using landmarks. Technical Report SCR-94-TR-474, Siemens Corporate Research, Princeton, NJ . Will also appear in the IEEE Transactions on Robotics and Automation.
11.
Borenstein, J. 1987 (June). The Nursing Robot System. Ph.D. thesis, Technion, Haifa, Israel.
12.
Borenstein, J., Everett, B., and Feng, L. 1996. Navigating Mobile Robots: Systems and Techniques. Wellesley, MA: A. K. Peters .
13.
Borenstein, J., and Koren, Y. 1991. The vector field histogram—Fast obstacle avoidance for mobile robots . IEEE Journal of Robotics and Automation7(3): 278–288 .
14.
Breazeal, C. (Ferrell). 1998. A motivational system for regulating human-robot interaction . Proceedings of AAAI’98, Madison, WI, pp. 54–61 .
15.
Brooks, R. A. 1989. A robot that walks; emergent behaviors from a carefully evolved network . Neural Computation1(2): 253-253 .
16.
Brooks, R. A. 1990. Elephants don’t play chess . Autonomous Robots6: 3–15 .
17.
Brooks, R. A. 1991. Intelligence without reason . Proceedings of IJCAI-91, Sydney, Australia, July, pp. 569–595 .
18.
Buhmann, J., Burgard, W., Cremers, A. B., Fox, D., Hofmann, T., Schneider, F., Strikos, J., and Thrun, S. 1995. The mobile robot Rhino . AI Magazine16(1).
19.
Burgard, W., Cremers, A. B., Fox, D., Hähnel, D., Lake-meyer, G., Schulz, D., Steiner, W., and Thrun, S. 1999. Experiences with an interactive museum tour-guide robot . Artificial Intelligence114(1-2): 3–55 .
20.
Burgard, W., Fox, D., Hennig, D., and Schmidt, T. 1996. Estimating the absolute position of a mobile robot using position probability grids . Proceedings of the Thirteenth National Conference on Artificial Intelligence, Menlo Park, CA, August, pp. 896–901 .
21.
Canny, J. 1987. The Complexity of Robot Motion Planning. Cambridge, MA: MIT Press .
22.
Castellanos, J. A., Montiel, J.M.M., Neira, J., and Tardós, J. D. 1999. The SPmap: A probabilistic framework for simultaneous localization and map building . IEEE Transactions on Robotics and Automation15(5): 948–953 .
23.
Castellanos, J. A., and Tardós, J. D. 2000. Mobile Robot Localization and Map Building: A Multisensor Fusion Approach. Boston, MA: Kluwer Academic .
24.
Choset, H. 1996. Sensor Based Motion Planning: The Hierarchical Generalized Voronoi Graph. Ph.D. thesis, California Institute of Technology.
25.
Choset, H., Konuksven, I., and Burdick, J. W. 1996. Sensor based planning for a planar rod robot . Proc. IEEE/SICE/RSJ Int. Conf. on Multisensor Fusion on Multisensor Fusion and Integration for Intelligent Systems, Washington, DC.
26.
Choset, H., Konuksven, I., and Rizzi, A. 1996. Sensor based planning: A control law for generating the generalized voronoi graph . Proc. IEEE Int. Advanced Robotics, Washington, DC.
27.
Chown, E., Kaplan, S., and Kortenkamp, D. 1995. Prototypes, location, and associative networks (plan): Towards a unified theory of cognitive mapping . Cognitive Science19: 1–51 .
28.
Connell, J. 1990. Minimalist Mobile Robotics. Boston, MA: Academic Press .
29.
Cox, I. J. 1991. Blanche—An experiment in guidance and navigation of an autonomous robot vehicle . IEEE Transactions on Robotics and Automation7(2): 193–204 .
30.
Cox, I.J., and Wilfong, G. T., eds. 1990. Autonomous Robot Vehicles. New York/Berlin: Springer-Verlag .
31.
Dautenhahn, K. 1997. The role of interactive conceptions of intelligence and life in cognitive technology . Proceedings of CT-97, pp. 33–43 .
32.
Dean, T. L., and Boddy, M. 1988. An analysis of time-dependent planning . Proceeding of Seventh National Conference on Artificial Intelligence AAAI-92, Menlo Park, CA, pp. 49–54 .
33.
Dean, T. L., and Kanazawa, K. 1989. A model for reasoning about persistence and causation . Computational Intelligence5(3): 142–150 .
34.
Dellaert, F., Burgard, W.Fox, D., and Thrun, S. 1999. Using the condensation algorithm for robust, vision-based mobile robot localization . Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Fort Collins, CO.
35.
Dellaert, F., Fox, D., Burgard, W., and Thrun, S. 1999. Monte carlo localization for mobile robots . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Detroit, MI, pp. 1322–1328 .
36.
Dellaert, F., Thorpe, C., and Thrun, S. 1999. Mosaicing a large number of widely dispersed, noisy, and distorted images: A Bayesian approach. Technical Report CMU-RITR-99-34, Carnegie Mellon University, Pittsburgh, PA .
37.
Dempster, A. P., Laird, A. N., and Rubin, D. B. 1977. Maximum likelihood from incomplete data via the EM algorithm . Journal of the Royal Statistical Society, Series B39(1): 1–38 .
38.
Dennis, J. E., and Schnabel, R. B. 1983. Numerical methods for unconstrained optimization and nonlinear equations. Englewood Cliffs, NJ: Prentice Hall .
39.
Denzler, J., Heigl, B., and Niemann, H. 1999. Combining computer graphics and computer vision for probabilistic self-localization. Internal report.
40.
Doucet, A. 1998. On sequential simulation-based methods for Bayesian filtering. Technical Report CUED/FINFENG/TR 310, Cambridge University, Department of Engineering, Cambridge, UK .
41.
Doucet, A., Gordon, N. J., and de Freitas, J.F.G., eds. 2000. Sequential Monte Carlo Methods in Practice. Forthcoming.
42.
Elfes, A. 1987. Sonar-based real-world mapping and navigation . IEEE Journal of Robotics and AutomationRA-3(3): 249–265 .
43.
Elfes, A. 1989. Occupancy Grids: A Probabilistic Framework for Robot Perception and Navigation. Ph.D. thesis, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA.
44.
Endres, H., Feiten, W., and Lawitzky, G. 1998. Field test of a navigation system: Autonomous cleaning in supermarkets . Proc. of the 1998 IEEE International Conference on Robotics & Automation (ICRA 98), Leuven, Belgium.
45.
Engelberger, G. 1999. Services. In Handbook of Industrial Robotics, 2d ed, Ed. Shimon Y. Nof, 1201–1212. New York: John Wiley .
46.
Engelson, S. 1994. Passive Map Learning and Visual Place Recognition. Ph.D. thesis, Department of Computer Science, Yale University, New Haven, CT.
47.
Fedor, C. 1993. TCX. An interprocess communication system for building robotic architectures. Programmer’s guide to version 10.xx. Carnegie Mellon University, Pittsburgh, PA .
48.
Fox, D., Burgard, W., Dellaert, F., and Thrun, S. 1999. Monte Carlo localization: Efficient position estimation for mobile robots . Proceedings of the National Conference on Artificial Intelligence (AAAI), Orlando, FL.
49.
Fox, D., Burgard, W., and Thrun, S. 1998. A hybrid collision avoidance method for mobile robots . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Leuven, Belgium.
50.
Fox, D., Burgard, W., and Thrun, S. 1999. Markov localization for mobile robots in dynamic environments . Journal of Artificial Intelligence Research11: 391–427 .
51.
Fukuda, T., Ito, S., Oota, N., Arai, F., Abe, Y., Tanake, K., and Tanaka, Y. 1993. Navigation system based on ceiling landmark recognition for autonomous mobile robot . Proc. Int’l Conference on Industrial Electronics Control and Instrumentation (IECON’93), vol. 1, pp. 1466–1471 .
52.
Goldberg, K., Mascha, M., Gentner, S., Rothenberg, N., Sutter, C., and Wiegley, J. 1995. Desktop tele-operation via the World Wide Web . Proceedings of the IEEE International Conference on Robotics and Automation, Nagoya, Japan.
53.
Goldberg, K., Santarromana, J., Bekey, G., Gentner, S., Morris, R., Wiegley, J., and Berger, E. 1995. The telegarden . Proc. of ACM SIGGRAPH.
54.
Goldberg, S., Bekey, G. A., and Akatsuka, Y. 1998. DIGIMUSE: An interactive telerobotic system for remote viewing of three-dimensional art objects . Proceedings of the IEEE IROS’98 Workshop on Robots on the Web, Victoria, Canada.
55.
Gutmann, J.-S., Burgard, W., Fox, D., and Konolige, K. 1998. An experimental comparison of localization methods . Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Victoria, Canada.
56.
Gutmann, J.-S., and Nebel, B. 1997. Navigation mobiler roboter mit laserscans. Autonome Mobile Systeme. Berlin: Springer-Verlag .
57.
Gutmann, J.-S., and Schlegel, C. 1996. Amos: Comparison of scan matching approaches for self-localization in indoor environments . Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots. IEEE Computer Society Press.
58.
Guzzoni, D., Cheyer, A., Julia, L., and Konolige, K. 1997. Many robots make short work . AI Magazine18(1): 55–64 .
59.
Haehnel, D., Burgard, W., and Lakemeyer, G. 1998. GOLEX: Bridging the gap between logic (GOLOG) and a real robot . Proceedings of the 22nd German Conference on Artificial Intelligence (KI 98), Bremen, Germany.
60.
Hinkel, R., and Knieriemen, T. 1988. Environment perception with a laser radar in a fast moving robot . Proceedings of Symposium on Robot Control, Karlsruhe, Germany, October, pp. 68.1–68.7 .
61.
Horswill, I. 1993. Polly: A vision-based artificial agent . Proceedings of the Eleventh National Conference on Artificial Intelligence (AAAI-93), Washington, DC.
62.
Horswill, I. 1994 (September). Specialization of perceptual processes. Technical Report AI TR-1511, MIT, AI Lab, Cambridge, MA .
63.
Howard, R. A. 1960. Dynamic Programming and Markov Processes. Cambridge, MA: MIT Press .
64.
Isard, M., and Blake, A. 1996. Contour tracking by stochastic propagation of conditional density . European Conference on Computer Vision, Cambridge, MA, pp. 343–356 .
65.
Isard, M., and Blake, A. 1998. Condensation: Conditional density propagation for visual tracking . International Journal of Computer Vision.
66.
Jaakkola, T., Singh, S. P., and Jordan, M. I. 1995. Reinforcement learning algorithm for partially observable decision problems. In Advances in Neural Information Processing Systems 7, ed. G. Tesauro, D. Touretzky, and T. Leen. Cambridge, MA: MIT Press .
67.
Jazwinsky, A. M. 1970. Stochastic Processes and Filtering Theory. New York: Academic Press .
68.
Kaelbling, L. P., Cassandra, A. R., and Kurien, J. A. 1996. Acting under uncertainty: Discrete Bayesian models for mobile-robot navigation . Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Osaka, Japan.
69.
Kaelbling, L. P., Littman, M. L., and Cassandra, A. R. 1998. Planning and acting in partially observable stochastic domains . Artificial Intelligence101(1-2): 99–134 .
70.
Kaelbling, L. P., Littman, M. L., and Moore, A. W. 1996. Reinforcement learning: A survey . Journal of Artificial Intelligence Research4: 237–285 .
71.
Kahn, R. E., Swain, M. J., Prokopowicz, P. N., and Firby, R. J. 1996. Gesture recognition using the Perseus architecture . Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, pp. 734–741 .
72.
Kalman, R. E. 1960. A new approach to linear filtering and prediction problems . Trans. ASME, Journal of Basic Engineering82: 35–45 .
73.
Kanazawa, K., Koller, D., and Russell, S. J. 1995. Stochastic simulation algorithms for dynamic probabilistic networks . Proceedings of the 11th Annual Conference on Uncertainty in AI, Montreal, Canada.
74.
King, S., and Weiman, C. 1990. Helpmate autonomous mobile robot navigation system . Proceedings of the SPIE Conference on Mobile Robots, Boston, MA, November, vol. 2352, pp. 190–198 .
75.
Koenig, S., and Simmons, R. 1996. Passive distance learning for robot navigation . In Proceedings of the Thirteenth International Conference on Machine Learning, ed. L. Saitta, Bari, Italy, pp. 266–274 .
76.
Kortenkamp, D., Bonasso, R. P., and Murphy, R., ed. 1998. AI-based Mobile Robots: Case Studies of Successful Robot Systems. Cambridge, MA: MIT Press .
77.
jKortenkamp, D., Huber, E., and Bonasso, P. 1996. Recognizing and interpreting gestures on a mobile robot . Proceedings of AAAI-96, Portland, OR, pp. 915–921 .
78.
Kortenkamp, D., and Weymouth, T. 1994. Topological mapping for mobile robots using a combination of sonar and vision sensing . Proceedings of the Twelfth National Conference on Artificial Intelligence, Menlo Park, CA, July, pp. 979–984 .
79.
Kuipers, B., and Byun, Y.-T. 1988. A robust qualitative method for spatial learning in unknown environments . Proceeding of Eighth National Conference on Artificial Intelligence AAAI-88, Menlo Park, CA.
80.
Kuipers, B., and Byun, Y.-T. 1991. A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations . Journal of Robotics and Autonomous Systems8: 47–63 .
81.
Lacey, G., and Dawson-Howe, K. M. 1998. The application of robotics to a mobility aid for the elderly blind . Robotics and Autonomous Systems23: 245–252 .
Lenser, S., and Veloso, M. 2000. Sensor resetting localization for poorly modelled mobile robots . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA, pp. 1225–1230 .
84.
Leonard, J. J., and Durrant-Whyte, H. F. 1992. Directed Sonar Sensing for Mobile Robot Navigation. Boston, MA: Kluwer Academic .
85.
Leonard, J. J., Durrant-Whyte, H. F., and Cox, I. J. 1992. Dynamic map building for an autonomous mobile robot . International Journal of Robotics Research11(4): 89–96 .
86.
Littman, M. L., Cassandra, A. R., and Kaelbling, L. P. 1995. Learning policies for partially observable environments: Scaling up . In Proceedings of the Twelfth International Conference on Machine Learning, ed. A. Prieditis and S. Russell, Tahoe City, CA.
87.
Liu, J., and Chen, R. 1998. Sequential Monte Carlo methods for dynamic systems . Journal of the American Statistical Association93-93 .
88.
Lu, F., and Milios, E. 1997. Globally consistent range scan alignment for environment mapping . Autonomous Robots4: 333–349 .
89.
Maeder, R. E. 1994. Ray tracing and graphics extensions . The Mathematica Journal4(3).
90.
Matarić c, M. J. 1990 (January). A Distributed Model for Mobile Robot Environment-Learning and Navigation. Master’s thesis, MIT, Cambridge, MA. Also available as MIT AI Lab Tech Report AITR-1228.
91.
Maybeck, P. S. 1990. The Kalman filter: An introduction to concepts. In Autonomous Robot Vehicles, ed. I. J. Cox and G. T. Wilfong. New York/Berlin: Springer-Verlag .
92.
McDermott, D. 1991. A reactive plan language. Research Report YALEU/DCS/RR-864, Yale University, New Haven, CT .
93.
McLachlan, G. J., and Krishnan, T. 1997. The EM Algorithm and Extensions. Wiley Series in Probability and Statistics. New York: John Wiley .
94.
Michel, O., Saucy, P., and Mondada, F. 1997. Khepontheweb: An experimental demonstrator in telerobotics and virtual reality . Proceedings of the IEEE International Conference on Virtual Systems and Multimedia (VSMM’97).
95.
Monahan, G. E. 1982. A survey of partially observable Markov decision processes: Theory, models, and algorithms . Management Science28(1): 1–16 .
96.
Moravec, H. P. 1988. Sensor fusion in certainty grids for mobile robots . AI Magazine, Summer, 61–74 .
97.
Nehmzow, U., Smithers, T., and Hallam, J. 1991. Location recognition in a mobile robot using self-organizing feature maps . In Information Processing in Autonomous Mobile Robots, ed. G. Schmidt. New York/Berlin: Springer-Verlag.
98.
Neven, H., and Schöner, G. 1996. Dynamics parametrically controlled by image correlations organize robot navigation . Biological Cybernetics75: 293–307 .
99.
Nourbakhsh, I. 1999. An affective mobile robot with a full-time job . Artificial Intelligence114(1–2): 95–124 .
100.
Nourbakhsh, I., Powers, R., and Birchfield, S. 1995. DERVISH an office-navigating robot . AI Magazine16(2): 53–60 .
Ogata, T., and Sugano, S. 1999a. Between humans and robots—Consideration of primitive language in robots . Proceedings of the Conference on Intelligent Robots and Systems (IROS’99), Kyongju, Korea, pp. 870–875 .
103.
Ogata, T., and Sugano, S. 1999b. Emotional communication between humans and the autonomous robot which has the emotion model . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA’99), Detroit, MI, pp. 3177–3182 .
104.
Peters, L., Surmann, H., Guo, S., Beck, K., and Huser, J. 1994. Moria fuzzy logik gesteuertes, autonomes fahrzeug. Internal report, GMD National Research Center for Information Technology, St. Augustin, Germany.
105.
Pitt, M., and Shephard, N. 1999. Filtering via simulation: Auxiliary particle filter . Journal of the American Statistical Association.
106.
Rabiner, L. R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition . Proceedings of the IEEE. IEEE Log Number 8825949.
107.
Rabiner, L. R., and Juang, B. H. 1986. An introduction to hidden Markov models . IEEE ASSP Magazine.
108.
Reif, J. H. 1979. Complexity of the mover’s problem and generalizations . Proceedings of the 20th IEEE Symposium on Foundations of Computer Science, pp. 421–427 .
109.
Rencken, W. D. 1993. Concurrent localisation and map building for mobile robots using ultrasonic sensors . Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Yokohama, Japan, July, pp. 2129–2197 .
110.
Rosenberg, C., and Angle, C. 1994 (December). IT: An interactive animatronic prototype. IS Robotics Inc. internal development project. Description available: http://www.cs.cmu.edu/%7Echuck/robotpg/itpg/.
111.
Rosenblatt, J. 1997 (January). DAMN: A Distributed Architecture for Mobile Navigation. Ph.D.thesis, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA. Technical Report CMU-RI-TR-97-01.
112.
Roy, N., Baltus, G., Fox, D., Gemperle, F., Goetz, J., Hirsch, T., Magaritis, D., Montemerlo, M., Pineau, J., Schulte, J., and Thrun, S. 2000. Towards personal service robots for the elderly . Proceedings of the Workshop on Interactive Robotics and Entertainment (WIRE), Pittsburgh, PA.
113.
Roy, N., Burgard, W., Fox, D., and Thrun, S. 1999. Coastal navigation: Robot navigation under uncertainty in dynamic environments . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Detroit, MI, pp. 35–40 .
114.
Roy, N., Pineau, J., and Thrun, S. 2000. Spoken dialogue management using probabilistic reasoning . Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL-2000), Hong Kong.
115.
Roy, N., and Thrun, S. 1999. Coastal navigation with mobile robot . Proceedings of the 12th Conference on Neural Information Processing Systems (NIPS), Denver, CO, pp. 1043–1049 .
116.
Rubin, D. B. 1988. Using the SIR algorithm to simulate posterior distributions. In Bayesian Statistics 3, ed. M. H. Bernardo, K. M. an DeGroot, D. V. Lindley, and A.F.M. Smith. Oxford, UK: Oxford University Press .
117.
Russell, S., and Norvig, P. 1995. Artificial Intelligence: A Modern Approach. Englewood Cliffs, NJ: Prentice Hall .
118.
Saucy, P., and Mondada, F. 1998. Khep On The Web: One year of access to a mobile robot on the Internet . Proceedings of the IEEE IROS’98 Workshop on Robots on the Web, Victoria, Canada.
119.
Schiele, B., and Crowley, J. 1994. A comparison of position estimation techniques using occupancy grids . Proceedings of the 1994 IEEE International Conference on Robotics and Automation, San Diego, CA, May, pp. 1628–1634 .
120.
Schneider, F. E. 1994 (December). Sensor interpretation und Kartenerstellung für mobile Roboter. Master’s thesis, Dept. of Computer Science III, University of Bonn, 53117 Bonn. In German.
121.
Schraft, R. D., and Schmierer, G. 2000. Service Robots. Natick, MA: A. K. Peters .
122.
Schulte, J., Rosenberg, C., and Thrun, S. 1999. Spontaneous short-term interaction with mobile robots in public places . Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Detroit, MI, pp. 1999–2005 .
123.
Schulz, D., Burgard, W., Cremers, A. B., Fox, D., and Thrun, S. 2000. Web interfaces for mobile robots in public places . IEEE Magazine on Robotics and Automation7(1): 48–57 .
124.
Schwartz, J. T., Scharir, M., and Hopcroft, J. 1987. Planning, Geometry and Complexity of Robot Motion. Norwood, NJ: Ablex .
125.
Shatkay, H. 1998. Learning Models for Robot Navigation. Ph.D. thesis, Computer Science Department, Brown University, Providence, RI.
126.
Shatkay, H., and Kaelbling, L. 1997. Learning topological maps with weak local odometric information . Proceedings of IJCAI-97, Nagoya, Japan, pp. 920–927 .
127.
Siegwart, R., and Goldberg, K., eds. 2000. Robots on the Web . IEEE Magazine on Robotics and Automation (special issue)7(1).
128.
Simmons, R. 1992. Concurrent planning and execution for autonomous robots . IEEE Control Systems12(1): 46–50 .
129.
Simmons, R. 1996. Where in the world is Xavier, the robot? Machine Perception5(1).
130.
Simmons, R., Goodwin, R., Haigh, K., Koenig, S., and O’Sullivan, J. 1997. A layered architecture for office delivery robots . Proceedings of the First International Conference on Autonomous Agents, Marina del Rey, CA, February.
131.
Simmons, R., and Koenig, S. 1995. Probabilistic robot navigation in partially observable environments . Proceedings of IJCAI-95, Montreal, Canada, August, pp. 1080–1087 .
132.
Smallwood, R. W., and Sondik, E. J. 1973. The optimal control of partially observable markov processes over a finite horizon . Operations Research21: 1071–1088 .
133.
Smith, R., Self, M., and Cheeseman, P. 1990. Estimating uncertain spatial relationships in robotics. In Autonomous Robot Vehicles, ed. I. J. Cox and G. T. Wilfong, 167–193. New York/Berlin: Springer-Verlag .
134.
Sondik, E.J. 1978. The optimal control of partially observable markov processes over the infinite horizon: Discounted costs . Operations Research26(2): 282–304 .
135.
Stein, M. R. 1998. Painting on the World Wide Web: The Puma Paint project . Proceedings of the IEEE IROS’98 Workshop on Robots on the Web, Victoria, Canada.
136.
Sutton, R. S., and Barto, A. G. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press .
137.
Taylor, K., and Trevelyan, J. 1995. Australia’s telerobot on the Web . Proceedings of the 26th International Symposium On Industrial Robots.
138.
Thathachar, M.A.L., and Sastry, P. S. 1986. Estimator algorithms for learning automata . Proceedings of the Platinum Jubilee Conference on Systems and Signal Processing, Bengalore, India.
139.
Thrun, S. 1997. To know or not to know: On the utility of models in mobile robotics . AI Magazine18(1): 47–54 .
140.
Thrun, S. 1998a. Bayesian landmark learning for mobile robot localization . Machine Learning33(1): 41–76 .
141.
Thrun, S. 1998b. Learning metric-topological maps for indoor mobile robot navigation . Artificial Intelligence99(1): 21–71 .
142.
Thrun, S. 2000. Probabilistic algorithms in robotics. AI Magazine. Forthcoming.
143.
Thrun, S., Fox, D., and Burgard, W. 1998. A probabilistic approach to concurrent mapping and localization for mobile robots . Machine Learning31: 29–53 . Also appeared in Autonomous Robots5: 253–271 .
144.
Thrun, S., Fox, D., and Burgard, W. 2000. Monte Carlo localization with mixture proposal distribution . Proceedings of the AAAI National Conference on Artificial Intelligence, Austin, TX, pp. 859–865 .
145.
Torrance, M. C. 1994 (January). Natural Communication with Robots. Master’s thesis, MIT Department of Electrical Engineering and Computer Science, Cambridge, MA.
146.
Waldherr, S., Thrun, S., Romero, R., and Margaritis, D. 1998. Template-based recognition of pose and motion gestures on a mobile robot . Proceedings of the AAAI Fifteenth National Conference on Artificial Intelligence, Madison, WI, pp. 977–982 .
147.
Weiß, G., Wetzler, C., and von Puttkamer, E. 1994. Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans . Proceedings of the International Conference on Intelligent Robots and Systems, pp. 595–601 .
148.
Yamauchi, B., and Langley, P. 1997. Place recognition in dynamic environments . Journal of Robotic Systems14(2): 107–120 .
149.
Yamauchi, B., Langley, P., Schultz, A. C., Grefenstette, J., and Adams, W. 1998 (May). Magellan: An integrated adaptive architecture for mobile robots. Technical Report 98-2, Institute for the Study of Learning and Expertise (ISLE), Palo Alto, CA .
150.
Zilberstein, S., and Russell, S. 1995. Approximate reasoning using anytime algorithms. In Imprecise and Approximate Computation, ed. S. Natarajan. Dordrecht, Germany: Kluwer .
151.
Zimmer, U. R. 1996. Robust world-modeling and navigation in a real world . Neurocomputing13(2–4): 21–71 .
