Active policy learning for robot planning and exploration under uncertainty

Robotics: Science and Systems

Volumn 3, Issue , 2008, Pages 321-328

  Martinez Cantin, Ruben ^a   De Freitas, Nando ^b   Doucet, Arnaud ^b   Castellanos, José A ^a  

^a UNIVERSITY OF ZARAGOZA   (Spain)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84959309936     PISSN: 23307668     EISSN: 2330765X     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. N. Kohl and P. Stone, "Policy gradient reinforcement learning for fast quadrupedal locomotion, " in IEEE International Conference on Robotics and Automation, 2004.
2. G. Lawrence, N. Cowan, and S. Russell, "Efficient gradient estimation for motor control learning, " in Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann, 2003, pp. 354-36.
3. A. Ng, A. Coates, M. Diel, V. Ganapathi, J. Schulte, B. Tse, E. Berger, and E. Liang, "Inverted autonomous helicopter flight via reinforcement learning, " in International Symposium on Experimental Robotics, 2004.
4. J. Peters and S. Schaal, "Policy gradient methods for robotics, " in IEEE International Conference on Intelligent Robotics Systems, 2006.
5. J. Baxter and P. L. Bartlett, "Infinite-horizon policy-gradient estimation, " Journal of Artificial Intelligence Research, vol. 15, pp. 319-350, 2001.
6. S. Singh, N. Kantas, A. Doucet, B. N. Vo, and R. J. Evans, "Simulationbased optimal sensor scheduling with application to observer trajectory planning, " in IEEE Conference on Decision and Control and European Control Conference, 2005, pp. 7296-7301.
7. R. Sim and N. Roy, "Global A-optimal robot exploration in SLAM, " in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Barcelona, Spain, 2005.
8. C. Stachniss, G. Grisetti, and W. Burgard, "Information gain-based exploration using Rao-Blackwellized particle filters, " in Proceedings of Robotics: Science and Systems, Cambridge, USA, June 2005.
9. S. Paris and J. P. Le Cadre, "Planification for terrain-aided navigation, " in Fusion 2002, Annapolis, Maryland, July 2002, pp. 1007-1014.
10. C. Leung, S. Huang, G. Dissanayake, and T. Forukawa, "Trajectory planning for multiple robots in bearing-only target localisation, " in IEEE International Conference on Intelligent Robotics Systems, 2005.
11. M. L. Hernandez, "Optimal sensor trajectories in bearings-only tracking, " in Proceedings of the Seventh International Conference on Information Fusion, P. Svensson and J. Schubert, Eds., vol. II. Mountain View, CA: International Society of Information Fusion, Jun 2004, pp. 893-900.
12. T. Kollar and N. Roy, "Using reinforcement learning to improve exploration trajectories for error minimization, " in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2006.
13. D. R. Jones, M. Schonlau, and W. J. Welch, "Efficient global optimization of expensive black-box functions, " Journal of Global Optimization, vol. 13, no. 4, pp. 455-492, 1998.
14. T. J. Santner, B. Williams, and W. Notz, The Design and Analysis of Computer Experiments. Springer-Verlag, 2003.
15. E. S. Siah, M. Sasena, and J. L. Volakis, "Fast parameter optimization of large-scale electromagnetic objects using DIRECT with Kriging metamodeling, " IEEE Transactions on Microwave Theory and Techniques, vol. 52, no. 1, pp. 276-285, January 2004.
16. L. P. Kaelbling, "Learning in embedded systems, " Ph.D. dissertation, Stanford University, 1990.
17. A. W. Moore and J. Schneider, "Memory-based stochastic optimization, " in Advances in Neural Information Processing Systems, D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, Eds., vol. 8. The MIT Press, 1996, pp. 1066-1072.
18. N. Bergman and P. Tichavský, "Two Cramér-Rao bounds for terrainaided navigation, " IEEE Transactions on Aerospace and Electronic Systems, 1999, in review.
19. M. L. Hernandez, T. Kirubarajan, and Y. Bar-Shalom, "Multisensor resource deployment using posterior Cramèr-Rao bounds, " IEEE Transactions on Aerospace and Electronic Systems Aerospace, vol. 40, no. 2, pp. 399-416, April 2004.
20. D. P. Bertsekas, Dynamic Programming and Optimal Control. Athena Scientific, 1995.
21. O. Tremois and J. P. LeCadre, "Optimal observer trajectory in bearingsonly tracking for manoeuvring sources, " IEE Proceedings on Radar, Sonar and Navigation, vol. 146, no. 1, pp. 31-39, 1999.
22. R. D. Smallwood and E. J. Sondik, "The optimal control of partially observable Markov processes over a finite horizon, " Operations Research, vol. 21, pp. 1071-1088, 1973.
23. R. J. Williams, "Simple statistical gradient-following algorithms for connectionist reinforcement learning, " Machine Learning, vol. 8, no. 3, pp. 229-256, 1992.
24. A. Y. Ng and M. I. Jordan, "Pegasus: A policy search method for large MDPs and POMDPs." in Uncertainty in Artificial Intelligence (UAI2000), 2000.
25. J. M. Maciejowski, Predictive control: with constraints. Prentice-Hall, 2002.
26. J. A. Castellanos and J. D. Tardós, Mobile Robot Localization and Map Building. A Multisensor Fusion Approach. Kluwer Academic Publishers, 1999.
27. H. J. Kushner, "A new method of locating the maximum of an arbitrary multipeak curve in the presence of noise, " Journal of Basic Engineering, vol. 86, pp. 97-106, 1964.
28. D. R. Jones, "A taxonomy of global optimization methods based on response surfaces, " Journal of Global Optimization, vol. 21, pp. 345-383, 2001.
29. M. J. Sasena, "Flexibility and efficiency enhancement for constrained global design optimization with Kriging approximations, " Ph.D. dissertation, University of Michigan, 2002.
30. D. R. Jones, C. D. Perttunen, and B. E. Stuckman, "Lipschitzian optimization without the Lipschitz constant, " Journal of Optimization Theory and Applications, vol. 79, no. 1, pp. 157-181, October 1993.
31. J. M. Gablonsky, "Modification of the DIRECT algorithm, " Ph.D. dissertation, Department of Mathematics, North Carolina State University, Raleigh, North Carolina, 2001.
32. D. E. Finkel, DIRECT Optimization Algorithm User Guide. Center for Research in Scientific Computation, North Carolina State University, 2003.
33. A. J. Smola, S. V. N. Vishwanathan, and T. Hofmann, "Kernel methods for missing variables, " in AI Stats, 2005, pp. 325-332.
34. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning. Cambridge, Massachusetts: MIT Press, 2006.
35. P. Tichavský, C. Muravchik, and A. Nehorai, "Posterior Cramér-Rao bounds for discrete-time nonlinear filtering, " IEEE Transactions on Signal Processing, vol. 46, no. 5, pp. 1386-1396, 1998.
36. N. Bergman, A. Doucet, and N. J. Gordon, "Optimal estimation and Cramér-Rao bounds for partial non-Gaussian state space models, " Annals of the Institute of Mathematical Statistics, vol. 52, no. 1, pp. 1-17, 2001.