Year |
Citation |
Score |
2019 |
Amato C, Konidaris G, Kaelbling LP, How JP. Modeling and Planning with Macro-Actions in Decentralized POMDPs. The Journal of Artificial Intelligence Research. 64: 817-859. PMID 31656393 DOI: 10.1613/Jair.1.11418 |
0.469 |
|
2018 |
Konidaris G, Kaelbling LP, Lozano-Perez T. From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning Journal of Artificial Intelligence Research. 61: 215-289. DOI: 10.1613/Jair.5575 |
0.366 |
|
2015 |
Garrett CR, Lozano-Pérez T, Kaelbling LP. Backward-forward search for manipulation planning Ieee International Conference On Intelligent Robots and Systems. 2015: 6366-6373. DOI: 10.1109/IROS.2015.7354287 |
0.317 |
|
2015 |
Amato C, Konidaris G, Cruz G, Maynor CA, How JP, Kaelbling LP. Planning for decentralized control of multiple robots under uncertainty Proceedings - Ieee International Conference On Robotics and Automation. 2015: 1241-1248. DOI: 10.1109/ICRA.2015.7139350 |
0.356 |
|
2015 |
Konidaris G, Kaelbling LP, Lozano-Perez T. Symbol acquisition for probabilistic high-level planning Ijcai International Joint Conference On Artificial Intelligence. 2015: 3619-3627. |
0.392 |
|
2015 |
Amato C, Konidaris G, Omidshafiei S, Agha-Mohammadi AA, How JP, Kaelbling LP. Probabilistic planning for decentralized multi-robot systems Aaai Fall Symposium - Technical Report. 10-12. |
0.314 |
|
2014 |
Konidaris G, Kaelbling LP, Lozano-Perez T. Constructing symbolic representations for high-level planning Proceedings of the National Conference On Artificial Intelligence. 3: 1932-1938B. |
0.41 |
|
2013 |
Kaelbling LP, Lozano-Pérez T. Integrated task and motion planning in belief space International Journal of Robotics Research. 32: 1194-1227. DOI: 10.1177/0278364913484072 |
0.322 |
|
2013 |
Levihn M, Kaelbling LP, Lozano-Perez T, Stilman M. Foresight and reconsideration in hierarchical planning and execution Ieee International Conference On Intelligent Robots and Systems. 224-231. DOI: 10.1109/IROS.2013.6696357 |
0.306 |
|
2013 |
Hadfield-Menell D, Kaelbling LP, Lozano-Perez T. Optimization in the now: Dynamic peephole optimization for hierarchical planning Proceedings - Ieee International Conference On Robotics and Automation. 4560-4567. DOI: 10.1109/ICRA.2013.6631225 |
0.362 |
|
2013 |
Konidaris G, Kaelbling LP, Lozano-Perez T. Symbol acquisition for task-level planning Aaai Workshop - Technical Report. 9-15. |
0.37 |
|
2012 |
Kaelbling LP, Lozano-Pérez T. Unifying perception, estimation and action for mobile manipulation via belief space planning Proceedings - Ieee International Conference On Robotics and Automation. 2952-2959. DOI: 10.1109/ICRA.2012.6225237 |
0.323 |
|
2012 |
Platt R, Kaelbling L, Lozano-Perez T, Tedrake R. Non-Gaussian belief space planning: Correctness and complexity Proceedings - Ieee International Conference On Robotics and Automation. 4711-4717. DOI: 10.1109/ICRA.2012.6225223 |
0.308 |
|
2012 |
Macindoe O, Kaelbling LP, Lozano-Pérez T. POMCoP: Belief space planning for sidekicks in cooperative games Proceedings of the 8th Aaai Conference On Artificial Intelligence and Interactive Digital Entertainment, Aiide 2012. 38-43. |
0.34 |
|
2012 |
Oliehoek FA, Witwicki SJ, Kaelbling LP. Influence-based abstraction for multiagent systems Proceedings of the National Conference On Artificial Intelligence. 2: 1422-1428. |
0.428 |
|
2012 |
Macindoe O, Kaelbling LP, Lozano-Perez T. Assistant agents for sequential planning problems Aaai Workshop - Technical Report. 27-30. |
0.416 |
|
2012 |
Witwicki SJ, Oliehoek FA, Kaelbling LP. Heuristic search of multiagent influence space 11th International Conference On Autonomous Agents and Multiagent Systems 2012, Aamas 2012: Innovative Applications Track. 1: 200-207. |
0.313 |
|
2010 |
Aha DW, Boddy M, Bulitko V, D'Avila Garcez AS, Doshi P, Edelkamp S, Geib C, Gmytrasiewicz P, Goldman RP, Halevy A, Hitzler P, Isbell C, Josyula D, Kaelbling LP, Kersting K, et al. Reports of the AAAI 2010 conference workshops Ai Magazine. 31: 95-104. DOI: 10.1609/Aimag.V31I4.2318 |
0.378 |
|
2009 |
Zettlemoyer LS, Milch B, Kaelbling LP. Multi-agent filtering with infinitely nested beliefs Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 1905-1912. |
0.37 |
|
2007 |
Pasula HM, Zettlemoyer LS, Kaelbling LP. Learning symbolic models of stochastic domains Journal of Artificial Intelligence Research. 29: 309-352. DOI: 10.1613/Jair.2113 |
0.373 |
|
2007 |
Gardiol NH, Kaelbling LP. Action-space partitioning for planning Proceedings of the National Conference On Artificial Intelligence. 2: 980-986. |
0.323 |
|
2007 |
Zarybnisky E, Armacost A, Kolitz S, Barnhart C, Kaelbling L. Allocation of air resources against an intelligent adversary Collection of Technical Papers - 2007 Aiaa Infotech At Aerospace Conference. 1: 918-947. |
0.312 |
|
2004 |
Gardiol NH, Kaelbling LP. Envelope-based planning in relational MDPs Advances in Neural Information Processing Systems. |
0.362 |
|
2004 |
Pasula HM, Zettlemoyer LS, Kaelbling LP. Learning probabilistic relational planning rules Proceedings of the 14th International Conference On Automated Planning and Scheduling, Icaps 2004. 73-81. |
0.308 |
|
2004 |
Chang YH, Ho T, Kaelbling LP. All learning is local: Multi-agent learning in global reward games Advances in Neural Information Processing Systems. |
0.3 |
|
2002 |
Shatkay H, Kaelbling LP. Learning geometrically-constrained Hidden Markov models for robot navigation: Bridging the topological-geometrical gap Journal of Artificial Intelligence Research. 16: 167-207. |
0.324 |
|
1998 |
Kaelbling LP, Littman ML, Cassandra AR. Planning and acting in partially observable stochastic domains Artificial Intelligence. 101: 99-134. DOI: 10.1016/S0004-3702(98)00023-X |
0.546 |
|
1996 |
Kaelbling LP, Littman ML, Moore AW. Reinforcement learning: A survey Journal of Artificial Intelligence Research. 4: 237-285. DOI: 10.1613/Jair.301 |
0.496 |
|
1996 |
Kaelbling LP, Littman ML, Cassandra AR. Partially observable Markov decision processes for artificial intelligencea Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1093: 146-163. DOI: 10.1007/BFb0013957 |
0.527 |
|
1995 |
Rosenschein SJ, Kaelbling LP. A situated view of representation and control Artificial Intelligence. 73: 149-173. DOI: 10.1016/0004-3702(94)00056-7 |
0.581 |
|
1995 |
Basye K, Dean T, Kaelbling LP. Learning dynamics: system identification for perceptually challenged agents Artificial Intelligence. 72: 139-171. DOI: 10.1016/0004-3702(94)00023-T |
0.365 |
|
1991 |
Kaelbling LP. A situated-automata approach to the design of embedded agents Acm Sigart Bulletin. 2: 85-88. DOI: 10.1145/122344.122361 |
0.406 |
|
1991 |
Kaelbling LP. Foundations of learning in autonomous agents Robotics and Autonomous Systems. 8: 131-144. DOI: 10.1016/0921-8890(91)90018-G |
0.373 |
|
1990 |
Kaelbling LP, Rosenschein SJ. Action and planning in embedded agents Robotics and Autonomous Systems. 6: 35-48. DOI: 10.1016/S0921-8890(05)80027-2 |
0.584 |
|
Low-probability matches (unlikely to be authored by this person) |
2011 |
Platt R, Tedrake R, Kaelbling L, Lozano-Pérez T. Belief space planning assuming maximum likelihood observations Robotics: Science and Systems. 6: 291-298. |
0.3 |
|
2005 |
Chang YH, Kaelbling LP. Hedged learning: Regret-minimization with learning experts Icml 2005 - Proceedings of the 22nd International Conference On Machine Learning. 121-128. |
0.291 |
|
1995 |
Dean T, Kaelbling LP, Kirman J, Nicholson A. Planning under time constraints in stochastic domains Artificial Intelligence. 76: 35-74. DOI: 10.1016/0004-3702(94)00086-G |
0.29 |
|
2014 |
Amato C, Konidaris GD, Kaelbling LP. Planning with macro-actions in decentralized POMDPs 13th International Conference On Autonomous Agents and Multiagent Systems, Aamas 2014. 2: 1273-1280. |
0.287 |
|
2008 |
Varshavskaya P, Kaelbling LP, Rus D. Automated design of adaptive controllers for modular robots using reinforcement learning International Journal of Robotics Research. 27: 505-526. DOI: 10.1177/0278364907084983 |
0.282 |
|
2010 |
Brunskill E, Kaelbling LP, Lozano-Pérez T, Roy N. Planning in partially-observable switching-mode continuous domains Annals of Mathematics and Artificial Intelligence. 58: 185-216. DOI: 10.1007/S10472-010-9202-1 |
0.282 |
|
2007 |
Hsiao K, Kaelbling LP, Lozano-Pérez T. Grasping POMDPs Proceedings - Ieee International Conference On Robotics and Automation. 4685-4692. DOI: 10.1109/ROBOT.2007.364201 |
0.278 |
|
2007 |
Deshpande A, Milch B, Zettlemoyer LS, Kaelbling LP. Learning probabilistic relational dynamics for multiple tasks Proceedings of the 23rd Conference On Uncertainty in Artificial Intelligence, Uai 2007. 83-92. |
0.278 |
|
2009 |
Varshavskaya P, Kaelbling LP, Rus D. Efficient distributed reinforcement learning through agreement Distributed Autonomous Robotic Systems 8. 367-378. DOI: 10.1007/978-3-642-00644-9-33 |
0.274 |
|
2002 |
Chang YH, Kaelbling LP. Playing is believing: The role of beliefs in multi-agent learning Advances in Neural Information Processing Systems. |
0.269 |
|
2011 |
Nguyen THD, Hsu D, Lee WS, Leong TY, Kaelbling LP, Lozano-Perez T, Grant AH. CAPIR: Collaborative action planning with intention recognition Proceedings of the 7th Aaai Conference On Artificial Intelligence and Interactive Digital Entertainment, Aiide 2011. 61-66. |
0.269 |
|
2019 |
Kim B, Kaelbling LP, Lozano-Pérez T. Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience Proceedings of the Aaai Conference On Artificial Intelligence. 33: 8017-8024. DOI: 10.1609/AAAI.V33I01.33018017 |
0.267 |
|
2014 |
Barragán PR, Kaelbling LP, Lozano-Pérez T. Interactive Bayesian identification of kinematic mechanisms Proceedings - Ieee International Conference On Robotics and Automation. 2013-2020. DOI: 10.1109/ICRA.2014.6907126 |
0.263 |
|
2004 |
Chang YH, Ho T, Kaelbling LP. Multi-agent learning in mobilized ad-hoc networks Aaai Fall Symposium - Technical Report. 81-88. |
0.263 |
|
2015 |
Lee G, Lozano-Pérez T, Kaelbling LP. Hierarchical planning for multi-contact non-prehensile manipulation Ieee International Conference On Intelligent Robots and Systems. 2015: 264-271. DOI: 10.1109/IROS.2015.7353384 |
0.263 |
|
2013 |
Barry J, Kaelbling LP, Lozano-Perez T. A hierarchical approach to manipulation with diverse actions Proceedings - Ieee International Conference On Robotics and Automation. 1799-1806. DOI: 10.1109/ICRA.2013.6630814 |
0.262 |
|
2004 |
Theocharous G, Kaelbling LP. Approximate planning in POMDPs with macro-actions Advances in Neural Information Processing Systems. |
0.262 |
|
2001 |
Lane T, Kaelbling LP. Approaches to macro decompositions of large Markov decision process planning problems Proceedings of Spie - the International Society For Optical Engineering. 4573: 104-113. DOI: 10.1117/12.457435 |
0.261 |
|
2014 |
Lozano-Pérez T, Kaelbling LP. A constraint-based method for solving sequential manipulation planning problems Ieee International Conference On Intelligent Robots and Systems. 3684-3691. DOI: 10.1109/IROS.2014.6943079 |
0.259 |
|
2016 |
Amato C, Konidaris G, Anders A, Cruz G, How JP, Kaelbling LP. Policy search for multi-robot coordination under uncertainty The International Journal of Robotics Research. 35: 1760-1778. DOI: 10.1177/0278364916679611 |
0.257 |
|
2005 |
Zettlemoyer LS, Pasula HM, Kaelbling LP. Learning planning rules in noisy stochastic worlds Proceedings of the National Conference On Artificial Intelligence. 2: 911-918. |
0.256 |
|
2010 |
Kaelbling LP, Lozano-Pérez T. Hierarchical task and motion planning in the now Aaai Workshop - Technical Report. 33-42. DOI: 10.1109/ICRA.2011.5980391 |
0.252 |
|
2008 |
Brunskill E, Kaelbling L, Lozano-Perez T, Roy N. Continuous-state POMDPs with hybrid dynamics 10th International Symposium On Artificial Intelligence and Mathematics, Isaim 2008. |
0.251 |
|
2012 |
Perez A, Platt R, Konidaris G, Kaelbling L, Lozano-Perez T. LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics Proceedings - Ieee International Conference On Robotics and Automation. 2537-2542. DOI: 10.1109/ICRA.2012.6225177 |
0.25 |
|
2008 |
Finney S, Kaelbling L, Lozano-Pérez T. Predicting partial paths from planning problem parameters Robotics: Science and Systems. 3: 41-48. |
0.249 |
|
1995 |
Dean T, Angluin D, Basye K, Engelson S, Kaelbling L, Kokkevis E, Maron O. Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning Machine Learning. 18: 81-108. DOI: 10.1023/A:1022874607797 |
0.241 |
|
2020 |
Kim B, Lee K, Lim S, Kaelbling L, Lozano-Perez T. Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds Proceedings of the Aaai Conference On Artificial Intelligence. 34: 9916-9924. DOI: 10.1609/aaai.v34i06.6546 |
0.237 |
|
2018 |
Garrett CR, Lozano-Pérez T, Kaelbling LP. Sampling-based methods for factored task and motion planning The International Journal of Robotics Research. 37: 1796-1825. DOI: 10.1177/0278364918802962 |
0.235 |
|
1994 |
Kaelbling LP. Associative Reinforcement Learning: A Generate and Test Algorithm Machine Learning. 15: 299-319. DOI: 10.1023/A:1022642026684 |
0.234 |
|
2011 |
Hsiao K, Kaelbling LP, Lozano-Pérez T. Robust grasping under object pose uncertainty Autonomous Robots. 31: 253-268. DOI: 10.1007/S10514-011-9243-2 |
0.234 |
|
1996 |
Mahadevan S, Kaelbling LP. The National Science Foundation Workshop on reinforcement learning Ai Magazine. 17: 89-97. |
0.232 |
|
2013 |
Holladay A, Barry J, Kaelbling LP, Lozano-Perez T. Object placement as inverse motion planning Proceedings - Ieee International Conference On Robotics and Automation. 3715-3721. DOI: 10.1109/ICRA.2013.6631099 |
0.231 |
|
1999 |
Moore AW, Baird LC, Kaelbling L. Multi-value-functions: Efficient automatic action hierarchies for multiple goal MDPs Ijcai International Joint Conference On Artificial Intelligence. 2: 1318-1321. |
0.231 |
|
2011 |
Hsiao K, Kaelbling LP, Lozano-Pérez T. Task-driven tactile exploration Robotics: Science and Systems. 6: 225-232. |
0.227 |
|
1994 |
Kaelbling LP. Associative Reinforcement Learning: Functions in k-DNF Machine Learning. 15: 279-298. DOI: 10.1023/A:1022689909846 |
0.224 |
|
2004 |
Theocharous G, Murphy K, Kaelbling LP. Representing hierarchical POMDPs as DBNs for multi-scale robot localization Proceedings - Ieee International Conference On Robotics and Automation. 2004: 1045-1051. |
0.219 |
|
2017 |
Garrett CR, Lozano-Pérez T, Kaelbling LP. FFRob: Leveraging symbolic planning for efficient task and motion planning The International Journal of Robotics Research. 37: 104-136. DOI: 10.1177/0278364917739114 |
0.219 |
|
1997 |
Shatkay H, Kaelbling LP. Learning topological maps with weak local odometric information Ijcai International Joint Conference On Artificial Intelligence. 2: 920-927. |
0.218 |
|
2015 |
Garrett CR, Lozano-Pérez T, Kaelbling LP. FFRob: An efficient heuristic for task and motion planning Springer Tracts in Advanced Robotics. 107: 179-195. DOI: 10.1007/978-3-319-16595-0_11 |
0.218 |
|
2019 |
Kim B, Wang Z, Kaelbling LP, Lozano-Pérez T. Learning to guide task and motion planning using score-space representation The International Journal of Robotics Research. 38: 793-812. DOI: 10.1177/0278364919848837 |
0.217 |
|
2010 |
Temizer S, Kochenderfer MJ, Kaelbling LP, Lozano-Pérez T, Kuchar JK. Collision avoidance for unmanned aircraft using Markov Decision Processes Aiaa Guidance, Navigation, and Control Conference. DOI: 10.2514/6.2010-8040 |
0.21 |
|
2015 |
Wong LLS, Kaelbling LP, Lozano-Pérez T. Data association for semantic world modeling from partial views International Journal of Robotics Research. 34: 1064-1082. DOI: 10.1177/0278364914559754 |
0.206 |
|
2014 |
Wong LLS, Kaelbling LP, Lozano-Pérez T. Not seeing is also believing: Combining object and metric spatial information Proceedings - Ieee International Conference On Robotics and Automation. 1253-1260. DOI: 10.1109/ICRA.2014.6907014 |
0.199 |
|
2018 |
Axelrod B, Kaelbling LP, Lozano-Pérez T. Provably safe robot navigation with obstacle uncertainty The International Journal of Robotics Research. 37: 1760-1774. DOI: 10.1177/0278364918778338 |
0.197 |
|
2002 |
Smart WD, Kaelbling LP. Effective reinforcement learning for mobile robots Proceedings - Ieee International Conference On Robotics and Automation. 4: 3404-3410. |
0.196 |
|
2004 |
Varshavskaya P, Kaelbling LP, Rus D. Learning distributed control for modular robots 2004 Ieee/Rsj International Conference On Intelligent Robots and Systems (Iros). 3: 2648-2653. |
0.194 |
|
2011 |
Barry JL, Kaelbling LP, Lozano-Pérez T. DetH*: Approximate hierarchical solution of large markov decision processes Ijcai International Joint Conference On Artificial Intelligence. 1928-1935. DOI: 10.5591/978-1-57735-516-8/IJCAI11-323 |
0.19 |
|
2013 |
Wong LLS, Kaelbling LP, Lozano-Perez T. Manipulation-based active search for occluded objects Proceedings - Ieee International Conference On Robotics and Automation. 2814-2819. DOI: 10.1109/ICRA.2013.6630966 |
0.183 |
|
2016 |
Nie X, Wong LLS, Kaelbling LP. Searching for physical objects in partially known environments Proceedings - Ieee International Conference On Robotics and Automation. 2016: 5403-5410. DOI: 10.1109/ICRA.2016.7487752 |
0.179 |
|
2007 |
Roy DM, Kaelbling LP. Efficient Bayesian task-level transfer learning Ijcai International Joint Conference On Artificial Intelligence. 2599-2604. |
0.176 |
|
2002 |
Lane T, Kaelbling LP. Nearly deterministic abstractions of Markov decision processes Proceedings of the National Conference On Artificial Intelligence. 260-266. |
0.172 |
|
2001 |
Smart WD, Kaelbling LP. Reinforcement learning for robot control Proceedings of Spie - the International Society For Optical Engineering. 4573: 92-103. DOI: 10.1117/12.457434 |
0.159 |
|
2001 |
Temizer S, Kaelbling LP. Holonomic planar motion from non-holonomic driving mechanisms: The front-point method Proceedings of Spie - the International Society For Optical Engineering. 4573: 56-67. DOI: 10.1117/12.457456 |
0.156 |
|
2012 |
Wong LLS, Kaelbling LP, Lozano-Pérez T. Collision-free state estimation Proceedings - Ieee International Conference On Robotics and Automation. 223-228. DOI: 10.1109/ICRA.2012.6225309 |
0.151 |
|
2020 |
Kaelbling LP. The foundation of efficient robot learning. Science (New York, N.Y.). 369: 915-916. PMID 32820109 DOI: 10.1126/science.aaz7597 |
0.148 |
|
2008 |
Milch B, Zettlemoyer LS, Kersting K, Haimes M, Kaelbling LP. Lifted probabilistic inference with counting formulas Proceedings of the National Conference On Artificial Intelligence. 2: 1062-1068. |
0.147 |
|
2007 |
Chiu HP, Kaelbling LP, Lozano-Pérez T. Virtual training for multi-view object class recognition Proceedings of the Ieee Computer Society Conference On Computer Vision and Pattern Recognition. DOI: 10.1109/CVPR.2007.383044 |
0.146 |
|
2019 |
Kawaguchi K, Huang J, Kaelbling LP. Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning. Neural Computation. 1-31. PMID 31614105 DOI: 10.1162/neco_a_01234 |
0.14 |
|
2005 |
Ross MG, Kaelbling LP. Learning static object segmentation from motion segmentation Proceedings of the National Conference On Artificial Intelligence. 2: 956-961. |
0.133 |
|
2010 |
Kaelbling LP. Technical perspective new bar set for intelligent vehicles Communications of the Acm. 53: 98. DOI: 10.1145/1721654.1721676 |
0.129 |
|
2004 |
Chang YH, Ho T, Kaelbling LP. Mobilized ad-hoc networks: A reinforcement learning approach Proceedings - International Conference On Autonomic Computing. 240-247. |
0.128 |
|
2009 |
Chiu HP, Kaelbling LP, Lozano-Pérez T. Learning to generate novel views of objects for class recognition Computer Vision and Image Understanding. 113: 1183-1197. DOI: 10.1016/J.Cviu.2009.06.004 |
0.122 |
|
1996 |
Kaelbling LP. Machine Learning. 22: 7-9. DOI: 10.1023/A:1018091703869 |
0.12 |
|
2015 |
Kawaguchi K, Kaelbling LP, Lozano-Pérez T. Bayesian optimization with exponential convergence Advances in Neural Information Processing Systems. 2015: 2809-2817. |
0.117 |
|
1998 |
Duchon AP, Kaelbling LP, Warren WH. Ecological robotics Adaptive Behavior. 6: 473-507. |
0.107 |
|
2019 |
Kawaguchi K, Huang J, Kaelbling LP. Effect of Depth and Width on Local Minima in Deep Learning. Neural Computation. 1-37. PMID 31120383 DOI: 10.1162/neco_a_01195 |
0.107 |
|
2014 |
Hollingsworth N, Meyer J, McGee R, Doering J, Konidaris G, Kaelbling L. Optimizing a start-stop controller using policy search Proceedings of the National Conference On Artificial Intelligence. 4: 2984-2989. |
0.096 |
|
2009 |
Ross MG, Kaelbling LP. Segmentation according to natural examples: learning static segmentation from motion segmentation. Ieee Transactions On Pattern Analysis and Machine Intelligence. 31: 661-76. PMID 19229082 DOI: 10.1109/TPAMI.2008.109 |
0.094 |
|
2010 |
Chiu HP, Liu H, Kaelbling LP, Lozano-Pérez T. Class-specific grasping of 3D objects from a single 2D image Ieee/Rsj 2010 International Conference On Intelligent Robots and Systems, Iros 2010 - Conference Proceedings. 579-585. DOI: 10.1109/IROS.2010.5652597 |
0.066 |
|
2010 |
Kersting K, Russell S, Kaelbling LP, Halevy A, Natarajan S, Mihalkova L. AAAI Workshop - Technical Report: Preface Aaai Workshop - Technical Report. vii. |
0.049 |
|
2014 |
Glover J, Kaelbling LP. Tracking the spin on a ping pong ball with the quaternion Bingham filter Proceedings - Ieee International Conference On Robotics and Automation. 4133-4140. DOI: 10.1109/ICRA.2014.6907460 |
0.01 |
|
2011 |
Wingate D, Goodman ND, Roy DM, Kaelbling LP, Tenenbaum JB. Bayesian policy search with policy priors Ijcai International Joint Conference On Artificial Intelligence. 1565-1570. DOI: 10.5591/978-1-57735-516-8/IJCAI11-263 |
0.01 |
|
Hide low-probability matches. |