Grasping POMDPs

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2007, Pages 4685-4692

  Hsiao, Kaijen ^a   Kaelbling, Leslie Pack ^a   Lozano Pérez, Tomás ^a  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PARTIALLY OBSERVABLE MARKOV DECISION PROCESS (POMDP); SIMPLE GRASPING PROBLEMS;

DECISION THEORY; MICROMANIPULATORS; MOTION PLANNING; OPTIMAL CONTROL SYSTEMS; ROBOTICS; ROBUST CONTROL;

UNCERTAINTY ANALYSIS;

EID: 36349037222     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2007.364201     Document Type: Conference Paper

References (29)

1. L. Brignone and M. Howarth. A geometrically validated approach to autonomous robotic assembly. IEEE/RSJ International Conference on Intelligent Robots and System, 2;1626-1631, 2002.
2. K. C. Brost and A. D. Christiansen. Probabilistic analysis of manipulation tasks: A computational, framework, international Journal of Robotics Research, 15(1):l-23, 1996.
3. Robert R. Burridge, Alfred A. Rizzi, and Daniel E. Koditschek Sequential Composition of dynamically dexterous robot behaviors. J. J. Robotic Res., 18(6),:534-555, 1999.
4. Anthony R. Cassandra, Leslie Pack Kaelbling, and James 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.
5. Bruce Donald. A geometric approach to error, detection, and recovery for robot motion planning, with uncertainty. Artificial Intelligence, 37:223-271, 1988.
6. Michael Efdmann, Using backprojection for fine motion planning with uncertainty. IJRR, 5(1):240-271, 1994.
7. Robert Givan, Thomas Dean, and Matthew Greig. Equivalence notions and model minimization in markov decision processes. Artif Intell., 147(1-2): 163-223, 2003.
8. R.A. Grupen and Jr. J.A. Coelho. Acquiring state from control dynamics to learn grasping policies for robot hands. Advanced Robotics, 16(5):427-443, 2002.
9. L. E. Kavraki, P. Svestka, J.-C.Latombe, and M.H. overmars. Probabilistic roadmaps for path planning in high-dimensional configuration spaces. IEEE Transactions On Robotics and Automation, 1.2(4):566-580, 1996.
10. J.C. Latombe. Robot Motion Planning. Kluwer Academic Publishers, Norwell, Mass, 1991.
11. Jean-Claude Latombe, Anthony Lazanas, and Shashank Shekhar. Robot motion planning with uncertainty in Control and sensing. Artif. Intell., 52(1): 1-47. 1991.
12. S. M. LaValle and S. A. Hutchinson, An Objective-based stochastic framework for manipulation planning. In Proc. IEEE/RSJ/GI Int'l Conf. on Intelligent Robots and Systems, pages 1772-1779, September 1994.
13. Steven, M. LaValle. Planning Algorithms. Cambridge University Press, 2006, to appear.
14. T. Lefebvre, H. Bruyninckx, and J. DeSchutter. Polyhedral contact formation identification for autonomous compliant motion; Exact nonlinear bayesian filtering. IEEE Transactions on Robotics, 21(.1):124-129, 2005.
15. Tomás Lozano-Pérez, Matthew Mason, and Russell H. Taylor. Automatic synthesis of fine-motion, strategies, for robots. International Journal of Robotics Research, 3(1), 1984.
16. Brenan J. McCarragher and Haruhiko Asada. A discrete event approach to the control of robotic assembly tasks. In ICRA, pages 331-336, 1993.
17. Nicolas Meuleau, Leonid Peshkin, Kee-Eung Kim, and Leslie Pack Kaelbling. Learning finite-state controllers for partially observable environments. In UAI, pages 427-436, 1999.
18. Wyatt S. Newman, Michael Branicky, Yoh-Han Pao, Craig Birkhimer, Siddharth Chhatpar, Jing Wei, and Yonghong Zhao. Intelligent strategies for compliant robotic assembly. In Proc. 11th Yale Workshop on Adaptive and Learning Systems, pages 139-146, 2001.
19. Andrew Y. Ng and Michael I. Jordan. Pegasus: A policy search method for large mdps and pomdps. In UAI, page 406, 2000.
20. Anya Petrovskaya, Oussama Khatib, Sebastian Thrun, and Andrew Y. Ng. Bayesian estimation for autonomous object manipulation based on tactile sensors. In ICRA, 2006.
21. J. Pineau, G. Gordon, and S, Thrun' Point-based value iteration: An anytime algorithm for pomdps. In IJCAI, 2003.
22. N. Roy and S. Thrun. Coastal navigation with mobile robot. In Proceedings of Conference on Neural Information Processing Systems (NIPS99), 2000.
23. Reid Simmons and Sven Koenig. Probabilistic robot navigation in partially observable, environments, In Proceedings of the International Joint Conference on Artificiai Intelligence, pages 1080-1087, 1995.
24. M. Skubic and R. A. Volz. Acquiring robust, force-based assembly skills from human demonstration. IEEE Transactions on.Robotics and Automation, 16(6):772-781, 2000.
25. Richard D. Smallwood and.Edward J, Sondik. The optimal control of partially observable Markov processes over a finite horizon. Operations Research, 21:1071-1088, 1973.
26. Trey Smith and Reid Simmons. Heuristic search value: iteration for pomdps, In AUAI '04: Proceedings of the 20th conference on Uncertainty in artificial intelligence, pages, 520-527, Arlington, Virginia, United States, 2004. AUAI Press.
27. M.T.J. Spaan and N. Vlassis, Perseus: randomized point-based value, iteration for pomdps. Journal of Artificial Intelligence Research, 24:195-220, 2005.
28. Richard S. Sutton, Doina Precup, and Satinder P. Singh. Between mdps and semi-mdps: A. framework for temporal abstraction in reinforcement learning. Artif. Intell, 112(1-2):181-211, 1999.
29. Georgias Theocharous and Leslie Pack Kaelbling. Approximate planning in, POMDPs with macro-actions. In Advances in Neural Information Processing Systems 16 (NIPS03), 2004.