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Research

Members of LIS work in a broad set of research areas. Here are some publications in areas of recent interest. Visit the Publications page for a full list of publications in reverse chronological order, across all areas.

Topics

// Integrated Task and Motion Planning

  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
[5] Learning value functions with relational state representations for guiding task-and-motion planning (Beomjoon Kim, Luke Shimanuki), In Conference on Robot Learning, 2019. BIBTEXPDF
[4] Learning Compact Models for Planning with Exogenous Processes (Rohan Chitnis and Tomas Lozano-Perez), In Conference on Robot Learning, 2019. BIBTEXPDF
[3] Learning Quickly to Plan Quickly Using Modular Meta-Learning (Rohan Chitnis and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Conference on Robotics and Automation (ICRA), 2019. BIBTEXPDF
[2] Adversarial actor-critic method for task and motion planning problems using planning experience (Beomjoon Kim and Leslie Pack Kaelbling and Tomas Lozano-Perez), In AAAI Conference on Artificial Intelligence (AAAI), 2019. BIBTEXPDF
[1] Learning to guide task and motion planning using score-space representation (Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, Tomas Lozano-Perez), In International Journal of Robotics Research, 2019. BIBTEXPDF
[6] Guiding Search in Continuous State-action Spaces by Learning an Action Sampler from Off-target Search Experience (Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez), In Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI). To appear, 2018. BIBTEXPDF
[5] From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning (Konidaris, George and Kaelbling, Leslie Pack and Lozano-Perez, Tomas), In Journal of Artificial Intelligence Research, volume 61, 2018. BIBTEXPDF
[4] Active model learning and diverse action sampling for task and motion planning (Zi Wang and Caelan Reed Garrett and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Conference on Intelligent Robots and Systems (IROS), 2018. BIBTEXPDF
[3] Sampling-based methods for factored task and motion planning (Caelan Reed Garrett, Tomas Lozano-Perez, Leslie Pack Kaelbling), In The International Journal of Robotics Research, 2018. BIBTEXPDF
[2] Look before you sweep: Visibility-aware motion planning (Gustavo Goretkin and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018. BIBTEXPDF
[1] Automated sequence and motion planning for robotic spatial extrusion of 3D trusses (Huang, Yijiangand Garrett, Caelan R.and Mueller, Caitlin T.), In Construction Robotics, volume 2, 2018. BIBTEXPDF
[4] Sample-Based Methods for Factored Task and Motion Planning (Caelan Reed Garrett and Tomas Lozano-Perez and Leslie Pack Kaelbling), In Robotics: Science and Systems (RSS), 2017. BIBTEXPDF
[3] Learning composable models of parameterized skills (Leslie Pack Kaelbling, Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2017. BIBTEXPDF
[2] Learning to guide task and motion planning using score-space representation (Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2017. BIBTEXPDF
[1] FFRob: Leveraging symbolic planning for efficient task and motion planning (Caelan Reed Garrett, Tomas Lozano-Perez, Leslie Pack Kaelbling), In The International Journal of Robotics Research, 2017. BIBTEXPDF
[5] Decidability of Semi-Holonomic Prehensile Task and Motion Planning (Ashwin Deshpande, Leslie Pack Kaelbling, Tomas Lozano-Perez), In International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2016. BIBTEXPDF
[4] Learning to Rank for Synthesizing Planning Heuristics (Caelan Reed Garrett and Leslie Pack Kaelbling and Tomas Lozano-Perez), In Int. Joint Conf. on Artificial Intelligence (IJCAI), 2016. BIBTEXPDF
[3] Searching for Physical Objects in Partially Known Environments (Xinkun Nie and Lawson L.S. Wong and Leslie Pack Kaelbling), In IEEE Conference on Robotics and Automation (ICRA), 2016. BIBTEXPDF
[2] Implicit Belief-Space Pre-images for Hierarchical Planning and Execution (Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2016. BIBTEXPDF
[1] Humanoid manipulation planning using backward-forward search (Grey, Michael X and Garrett, Caelan R and Liu, C Karen and Ames, Aaron D and Thomaz, Andrea L), In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, 2016. BIBTEXPDF
[2] Backward-Forward Search for Manipulation Planning (Caelan Reed Garrett and Tomas Lozano-Perez and Leslie Pack Kaelbling), In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. BIBTEXPDF
[1] Symbol Acquisition for Probabilistic High-Level Planning (G.D. Konidaris and L.P. Kaelbling and T. Lozano-Perez), In Proceedings of the Twenty Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2015. BIBTEXPDF
[3] FFRob: An efficient heuristic for task and motion planning (Caelan Reed Garrett and Tomas Lozano-Perez and Leslie Pack Kaelbling), In International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2014. BIBTEXPDF
[2] A constraint-based method for solving sequential manipulation planning problems (Tomas Lozano-Perez and Leslie Pack Kaelbling), In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014. BIBTEXPDF
[1] Constructing Symbolic Representations for High-Level Planning (George D. Konidaris and Leslie Kaelbling and Tomas Lozano-Perez), In Proceedings of the Twenty-Eighth Conference on Artificial Intelligence, 2014. BIBTEXPDF
[5] Foresight and Reconsideration in Hierarchical Planning and Execution (Martin Levihn and Leslie Pack Kaelbling and Tomas Lozano-Perez and Mike Stilman), In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013. BIBTEXPDF
[4] Optimal Sampling-Based Planning for Linear-Quadratic Kinodynamic Systems (G. Goretkin and A. Perez and R. Platt and G.D. Konidaris), In IEEE Conference on Robotics and Automation (ICRA), 2013. BIBTEXPDF
[3] Integrated Task and Motion Planning in Belief Space (Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Journal of Robotics Research, volume 32, 2013. BIBTEXPDF
[2] Manipulation-based Active Search for Occluded Objects (Lawson L.S. Wong and Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2013. BIBTEXPDF
[1] Optimization in the Now: Dynamic Peephole Optimization for Hierarchical Planning (Dylan Hadfield-Menell and Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2013. BIBTEXPDF
[1] Unifying Perception, Estimation and Action for Mobile Manipulation via Belief Space Planning (Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2012. BIBTEXPDF
[2] Pre-image backchaining in belief space for mobile manipulation (Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Symposium on Robotics Research (ISRR), 2011. BIBTEXPDF
[1] Hierarchical Task and Motion Planning in the Now (Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2011. BIBTEXPDF

// Belief Space Planning

  • 2018
  • 2017
  • 2016
  • 2013
  • 2012
  • 2011
  • 2010
[3] Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization (Rohan Chitnis and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Conference on Intelligent Robots and Systems (IROS), 2018. BIBTEXPDF
[2] Provably Safe Robot Navigation with Obstacle Uncertainty (Brian Axelrod and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Journal of Robotics Research, 2018. BIBTEXPDF
[1] Hardness of 3D Motion Planning Under Obstacle Uncertainty (Luke Shimanuki and Brian Axelrod), In International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2018. BIBTEXPDF
[1] Provably Safe Robot Navigation with Obstacle Uncertainty (Brian Axelrod and Leslie Pack Kaelbling and Tomas Lozano-Perez), In Robotics: Science and Systems (RSS), 2017. BIBTEXPDF
[1] Implicit Belief-Space Pre-images for Hierarchical Planning and Execution (Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2016. BIBTEXPDF
[1] Automatic Synthesis of Rules for Planning in Belief Space (Leslie Pack Kaelbling), In RSS Workshop on Combined Robot Motion Planning and AI Planning for Practical Applications, 2013. BIBTEXPDF
[3] Unifying Perception, Estimation and Action for Mobile Manipulation via Belief Space Planning (Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2012. BIBTEXPDF
[2] Non-Gaussian Belief Space Planning: Correctness and Complexity (Robert Platt and Leslie Kaelbling and Tomas Lozano-Perez and Russ Tedrake), In IEEE Conference on Robotics and Automation (ICRA), 2012. BIBTEXPDF
[1] LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristics (A. Perez and R. Platt and G.D. Konidaris and L.P. Kaelbling and T. Lozano-Perez), In Proceedings of the IEEE International Conference on Robotics and Automation, 2012. BIBTEXPDF
[3] Pre-image backchaining in belief space for mobile manipulation (Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Symposium on Robotics Research (ISRR), 2011. BIBTEXPDF
[2] Efficient planning in non-Gaussian belief spaces and its application to robot grasping (Robert Platt and Leslie Pack Kaelbling and Tomas Lozano-Perez and Russ Tedrake), In International Symposium on Robotics Research (ISRR), 2011. BIBTEXPDF
[1] Efficient planning in non-Gaussian belief spaces and its application to robot grasping (Robert Platt and Leslie Pack Kaelbling and Tomas Lozano-Perez and Russ Tedrake), In International Symposium on Robotics Research (ISRR), 2011. BIBTEXPDF
[1] Belief space planning assuming maximum likelihood observations (Robert Platt and Russell Tedrake and Leslie Kaelbling and Tomas Lozano-Perez), In Robotics Science and Systems Conference (RSS), 2010. BIBTEXPDF

// Learning and Optimization

  • 2020
  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
[5] Few-shot Bayesian Imitation Learning with Logical Program Policies (Silver, Tom and Allen, Kelsey R. and Lew, Alex K. and Kaelbling, Leslie Pack and Tenenbaum, Josh), In AAAI Conference on Artificial Intelligence (AAAI), 2020. BIBTEXPDF
[4] Monte Carlo Tree Search in continuous spaces using Voronoi optimistic optimization with regret bounds (Beomjoon Kim, Kyungjae Lee, Sungbin Lim, Leslie Pack Kaelbling, Tomas Lozano-Perez), In AAAI Conference on Artificial Intelligence (AAAI), 2020. BIBTEXPDF
[3] Ordered SGD: A New Stochastic Optimization Framework for Empirical Risk Minimization (Kenji Kawaguchi and Haihao Lu), In 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. BIBTEXPDF
[2] Elimination of All Bad Local Minima in Deep Learning (Kenji Kawaguchi and Leslie Pack Kaelbling), In 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 2020. BIBTEXPDF
[1] Meta-learning curiosity algorithms (Alet, Ferran and Schneider, Martin and Lozano-Perez, Tomas and Kaelbling, Leslie Pack), In International Conference on Learning Representations (ICLR), 2020. BIBTEXPDF
[15] Neural Relational Inference with Fast Modular Meta-learning (Alet, Ferran and Weng, Erica and Lozano-Perez, Tomas and Kaelbling, Leslie Pack), , 2019. BIBTEXPDF
[14] Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning (Kenji Kawaguchi, Jiaoyang Huang and Leslie Pack Kaelbling), In Neural Computation, volume 31, 2019. BIBTEXPDF
[13] Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes (Kenji Kawaguchi and Jiaoyang Huang), In 57th Allerton Conference on Communication, Control, and Computing (Allerton), 2019. BIBTEXPDF
[12] Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video (Maria Bauza and Ferran Alet and Yen-Chen Lin and Tomas Lozano-Perez and Leslie P. Kaelbling and Phillip Isola and Alberto Rodriguez), In International Conference on Intelligent Robots and Systems (IROS), 2019. BIBTEXPDF
[11] A Lagrangian Method for Inverse Problems in Reinforcement Learning (Pierre-Luc Bacon, Florian Schaefer, Clement Gehring, Animashree Anandkumar, Emma Brunskill), In NeurIPS Optimization Foundations for Reinforcement Learning Workshop, 2019. BIBTEXPDF
[10] Learning value functions with relational state representations for guiding task-and-motion planning (Beomjoon Kim, Luke Shimanuki), In Conference on Robot Learning, 2019. BIBTEXPDF
[9] Learning Compact Models for Planning with Exogenous Processes (Rohan Chitnis and Tomas Lozano-Perez), In Conference on Robot Learning, 2019. BIBTEXPDF
[8] Differentiable Algorithm Networks for Composable Robot Learning (Peter Karkus and Xiao Ma and David Hsu and Leslie Pack Kaelbling and Wee Sun Lee and Tomas Lozano-Perez), In Robotics: Science and Systems (RSS), 2019. BIBTEXPDF
[7] Learning Quickly to Plan Quickly Using Modular Meta-Learning (Rohan Chitnis and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Conference on Robotics and Automation (ICRA), 2019. BIBTEXPDF
[6] Adversarial actor-critic method for task and motion planning problems using planning experience (Beomjoon Kim and Leslie Pack Kaelbling and Tomas Lozano-Perez), In AAAI Conference on Artificial Intelligence (AAAI), 2019. BIBTEXPDF
[5] Learning sparse relational transition models (Victoria Xia and Zi Wang and Kelsey Allen and Tom Silver and Leslie Pack Kaelbling), In International Conference on Learning Representations (ICLR), 2019. BIBTEXPDF
[4] Depth with Nonlinearity Creates No Bad Local Minima in ResNets (Kenji Kawaguchi and Yoshua Bengio), In Neural Networks, volume 118, 2019. BIBTEXPDF
[3] Effect of Depth and Width on Local Minima in Deep Learning (Kenji Kawaguchi, Jiaoyang Huang and Leslie Pack Kaelbling), In Neural Computation, volume 31, 2019. BIBTEXPDF
[2] Graph Element Networks: adaptive, structured computation and memory (Alet, Ferran and Jeewajee, Adarsh Keshav and Villalonga, Maria Bauza and Rodriguez, Alberto and Lozano-Perez, Tomas and Kaelbling, Leslie), In Proceedings of the 36th International Conference on Machine Learning, volume 97, 2019. BIBTEXPDF
[1] Learning to guide task and motion planning using score-space representation (Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, Tomas Lozano-Perez), In International Journal of Robotics Research, 2019. BIBTEXPDF
[8] Batched Large-scale Bayesian Optimization in High-dimensional Spaces (Zi Wang, Clement Gehring, Pushmeet Kohli, Stefanie Jegelka), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2018. BIBTEXPDF
[7] Deep Semi-Random Features for Nonlinear Function Approximation (Kenji Kawaguchi, Bo Xie and Le Song), In Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI), 2018. BIBTEXPDF
[6] Guiding Search in Continuous State-action Spaces by Learning an Action Sampler from Off-target Search Experience (Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez), In Proceedings of the 32th AAAI Conference on Artificial Intelligence (AAAI). To appear, 2018. BIBTEXPDF
[5] Generalization in Deep Learning (Kenji Kawaguchi, Leslie Pack Kaelbling, and Yoshua Bengio), In Mathematics of Deep Learning, Cambridge University Press, to appear. Prepint avaliable as: MIT-CSAIL-TR-2018-014, Massachusetts Institute of Technology, 2018. BIBTEXPDF
[4] Active model learning and diverse action sampling for task and motion planning (Zi Wang and Caelan Reed Garrett and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Conference on Intelligent Robots and Systems (IROS), 2018. BIBTEXPDF
[3] Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing (Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie Pack Kaelbling, Joshua B. Tenenbaum and Alberto Rodriguez), In International Conference on Intelligent Robots and Systems (IROS), 2018. BIBTEXPDF
[2] Modular meta-learning (Ferran Alet, Tomas Lozano-Perez, Leslie Pack Kaelbling), In Conference on Robot Learning (CoRL), 2018. BIBTEXPDF
[1] Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior (Zi Wang and Beomjoon Kim and Leslie Pack Kaelbling), In Neural Information Processing Systems (NeurIPS), 2018. BIBTEXPDF
[5] Learning composable models of parameterized skills (Leslie Pack Kaelbling, Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2017. BIBTEXPDF
[4] Learning to guide task and motion planning using score-space representation (Beomjoon Kim, Leslie Pack Kaelbling, Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2017. BIBTEXPDF
[3] Batched High-dimensional Bayesian Optimization via Structural Kernel Learning (Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli), In International Conference on Machine Learning (ICML), 2017. BIBTEXPDF
[2] Max-value Entropy Search for Efficient Bayesian Optimization (Zi Wang and Stefanie Jegelka), In International Conference on Machine Learning (ICML), 2017. BIBTEXPDF
[1] Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems (Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2017. BIBTEXPDF
[4] Optimization as Estimation with Gaussian Processes in Bandit Settings (Zi Wang, Bolei Zhou, Stefanie Jegelka), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. BIBTEXPDF
[3] Optimization as Estimation with Gaussian Processes in Bandit Settings (Zi Wang, Bolei Zhou, Stefanie Jegelka), In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. BIBTEXPDF
[2] Deep Learning without Poor Local Minima (Kenji Kawaguchi), In Advances in Neural Information Processing Systems (NeurIPS), 2016. BIBTEXPDF
[1] Global Continuous Optimization with Error Bound and Fast Convergence (Kenji Kawaguchi, Yu Maruyama and Xiaoyu Zheng), In Journal of Artificial Intelligence Research (JAIR), volume 56, 2016. BIBTEXPDF
[2] Generalizing over Uncertain Dynamics for Online Trajectory Generation (Beomjoon Kim and Albert Kim and Hongkai Dai and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Symposium of Robotics Research (ISRR), 2015. BIBTEXPDF
[1] Bayesian optimization with exponential convergence (Kenji Kawaguchi and Leslie Pack Kaelbling and Tomas Lozano-Perez), In Advances in Neural Information Processing Systems (NeurIPS), 2015. BIBTEXPDF

// State Estimation

  • 2019
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
[2] Neural Relational Inference with Fast Modular Meta-learning (Alet, Ferran and Weng, Erica and Lozano-Perez, Tomas and Kaelbling, Leslie Pack), , 2019. BIBTEXPDF
[1] Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video (Maria Bauza and Ferran Alet and Yen-Chen Lin and Tomas Lozano-Perez and Leslie P. Kaelbling and Phillip Isola and Alberto Rodriguez), In International Conference on Intelligent Robots and Systems (IROS), 2019. BIBTEXPDF
[1] Object-based World Modeling in Semi-Static Envrionments with Dependent Dirichlet Process Mixtures (Lawson L.S. Wong and Thanard Kurutach and Tomas Lozano-Perez and Leslie Pack Kaelbling), In International Joint Conference on Artificial Intelligence (IJCAI), 2016. BIBTEXPDF
[1] Data association for semantic world modeling from partial views (Lawson L.S. Wong and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Journal of Robotics Research, volume 34, 2015. BIBTEXPDF
[3] Tracking the Spin on a Ping Pong Ball with the Quaternion Bingham Filter (Jared Glover and Leslie Pack Kaelbling), In IEEE Conference on Robotics and Automation (ICRA), 2014. BIBTEXPDF
[2] Interactive Bayesian Identification of Kinematic Mechanisms (Patrick R. Barragan and Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2014. BIBTEXPDF
[1] Not Seeing is Also Believing: Combining Object and Metric Spatial Information (Lawson L.S. Wong and Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2014. BIBTEXPDF
[2] Data association for semantic world modeling from partial views (Lawson L.S. Wong and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Symposium of Robotics Research, 2013. BIBTEXPDF
[1] Constructing Semantic World Models from Partial Views (Lawson L.S. Wong and Leslie Pack Kaelbling and Tomas Lozano-Perez), In Robotics: Science and Systems (RSS) Workshop on Robots in Clutter, 2013. BIBTEXPDF
[1] Collision-Free State Estimation (Lawson L.S. Wong and Leslie P. Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2012. BIBTEXPDF

// Reinforcement Learning

  • 2020
  • 2019
  • 2018
  • 2015
  • 2014
  • 2012
  • 2011
  • 2000
[1] Few-shot Bayesian Imitation Learning with Logical Program Policies (Silver, Tom and Allen, Kelsey R. and Lew, Alex K. and Kaelbling, Leslie Pack and Tenenbaum, Josh), In AAAI Conference on Artificial Intelligence (AAAI), 2020. BIBTEXPDF
[1] A Lagrangian Method for Inverse Problems in Reinforcement Learning (Pierre-Luc Bacon, Florian Schaefer, Clement Gehring, Animashree Anandkumar, Emma Brunskill), In NeurIPS Optimization Foundations for Reinforcement Learning Workshop, 2019. BIBTEXPDF
[2] From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning (Konidaris, George and Kaelbling, Leslie Pack and Lozano-Perez, Tomas), In Journal of Artificial Intelligence Research, volume 61, 2018. BIBTEXPDF
[1] Adaptable replanning with compressed linear action models for learning from demonstrations (Clement Gehring, Leslie Pack Kaelbling, Tomas Lozano-Perez), In Conference on Robot Learning (CoRL), 2018. BIBTEXPDF
[1] Symbol Acquisition for Probabilistic High-Level Planning (G.D. Konidaris and L.P. Kaelbling and T. Lozano-Perez), In Proceedings of the Twenty Fourth International Joint Conference on Artificial Intelligence (IJCAI), 2015. BIBTEXPDF
[2] Generalizing Policy Advice with Gaussian Process Bandits for Dynamic Skill Improvement (Jared Glover and Charlotte Zhu), In Proceedings of the Twenty-Eighth Conference on Artificial Intelligence, 2014. BIBTEXPDF
[1] Constructing Symbolic Representations for High-Level Planning (George D. Konidaris and Leslie Kaelbling and Tomas Lozano-Perez), In Proceedings of the Twenty-Eighth Conference on Artificial Intelligence, 2014. BIBTEXPDF
[5] Robot Learning from Demonstration by Constructing Skill Trees (G.D. Konidaris and S.R. Kuindersma and R.A. Grupen and A.G. Barto), In International Journal of Robotics Research, volume 31, 2012. BIBTEXPDF
[4] Learning Parameterized Skills (B.C. da Silva and G.D. Konidaris and A.G. Barto), In Proceedings of the Twenty Ninth International Conference on Machine Learning, 2012. BIBTEXPDF
[3] Transfer in Reinforcement Learning via Shared Features (G.D. Konidaris and I. Scheidwasser and A.G. Barto), In Journal of Machine Learning Research, volume 13, 2012. BIBTEXPDF
[2] Learning and Generalization of Complex Tasks from Unstructured Demonstrations (S. Niekum and S. Osentoski and G.D. Konidaris and A.G. Barto), In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012. BIBTEXPDF
[1] Transfer Learning by Discovering Latent Task Parametrizations (F. Doshi-Velez and G.D. Konidaris), In NIPS 2012 Workshop on Bayesian Nonparametric Models for Reliable Planning And Decision-Making Under Uncertainty, 2012. BIBTEXPDF
[3] TDγ: Re-evaluating Complex Backups in Temporal Difference Learning (G.D. Konidaris and S. Niekum and P.S. Thomas), In Advances in Neural Information Processing Systems 24, 2011. BIBTEXPDF
[2] Value Function Approximation in Reinforcement Learning using the Fourier Basis (G.D. Konidaris and S. Osentoski and P.S. Thomas), In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence, 2011. BIBTEXPDF
[1] Autonomous Skill Acquisition on a Mobile Manipulator (G.D. Konidaris and S.R. Kuindersma and R.A. Grupen and A.G. Barto), In Proceedings of the Twenty-Fifth Conference on Artificial Intelligence, 2011. BIBTEXPDF
[1] Practical Reinforcement Learning (William D. Smart and Leslie Pack Kaelbling), In International Conference on Machine Learning (ICML), 2000. BIBTEXPDF

// Manipulation Planning

  • 2019
  • 2018
  • 2015
  • 2013
  • 2012
  • 1992
[1] Force-And-Motion Constrained Planning for Tool Use (Rachel Holladay and Tomas Lozano-Perez and Alberto Rodriguez), In International Conference on Intelligent Robots and Systems (IROS), 2019. BIBTEXPDF
[1] Reliably arranging objects in uncertain domains (Ariel S. Anders and Leslie P. Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2018. BIBTEXPDF
[1] Hierarchical planning for multi-contact non-prehensile manipulation (Gilwoo Lee and Tomas Lozano-Perez and Leslie Pack Kaelbling), In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. BIBTEXPDF
[2] A Hierarchical Approach to Manipulation with Diverse Actions (Jennifer Barry and Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2013. BIBTEXPDF
[1] Object Placement as Inverse Motion Planning (Anne Holladay and Jennifer Barry and Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE Conference on Robotics and Automation (ICRA), 2013. BIBTEXPDF
[1] Manipulation with Multiple Action Types (J. Barry and K. Hsiao and L. P. Kaelbling and T. Lozano-Perez), In International Symposium on Experimental Robotics, 2012. BIBTEXPDF
[1] HANDEY: A Robot Task Planner (Tomas Lozano-Perez and Joseph L. Jones and Emanuel Mazer and Patrick A. O'Donnell), MIT Press, 1992. BIBTEXPDF

// Multiagent Planning

  • 2019
  • 2017
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
  • 2004
  • 2000
[1] Modeling and Planning with Macro-Actions in Decentralized POMDPs (Christopher Amato and George Konidaris and Leslie Pack Kaelbling and Jonathan P. How), In Journal of Artificial Intelligence Research, volume 64, 2019. BIBTEXPDF
[1] Policy search for multi-robot coordination under uncertainty (Christopher Amato and George Konidaris and Ariel Anders and Gabriel Cruz and Jonathan P How and Leslie P Kaelbling), In The International Journal of Robotics Research, volume 35, 2017. BIBTEXPDF
[2] Planning for Decentralized Control of Multiple Robots Under Uncertainty (C. Amato and G.D. Konidaris and G. Cruz and C. Maynor and J.P. How and L.P. Kaelbling), In Proceedings of the 2015 IEEE International Conference on Robotics and Automation, 2015. BIBTEXPDF
[1] Policy Search for Multi-Robot Coordination under Uncertainty (C. Amato and G.D. Konidaris and A. Anders and G. Cruz and J.P. How and L.P. Kaelbling), In Robotics: Science and Systems XI (RSS), 2015. BIBTEXPDF
[2] Planning with Macro-Actions in Decentralized POMDPs (Christopher Amato and George Konidaris and Leslie Pack Kaelbling), In Thirteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-14), 2014. BIBTEXPDF
[1] Exploiting Separability in Multi-Agent Planning with Continuous-State MDPs. (Jilles S. Dibangoye and Christopher Amato and Olivier Buffet and Francois Charpillet), In Thirteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-14), 2014. BIBTEXPDF
[3] Producing Efficient Error-bounded Solutions for Transition Independent Decentralized MDPs (Jilles S. Dibangoye and Christopher Amato and Arnaud Doniec and Francois Charpillet), In Proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems, 2013. BIBTEXPDF
[2] Incremental Clustering and Expansion for Faster Optimal Planning in Decentralized POMDPs (Frans A. Oliehoek and Matthijs T. J. Spaan and Christopher Amato and Shimon Whiteson), In Journal of Artificial Intelligence Research, volume 46, 2013. BIBTEXPDF
[1] Optimally Solving Dec-POMDPs as Continuous-State MDPs (Jilles S. Dibangoye and Christopher Amato and Olivier Buffet and Francois Charpillet), In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, 2013. BIBTEXPDF
[4] Heuristic Search of Multiagent Influence Space (Stefan Witwicki andFrans A. Oliehoek and Leslie P. Kaelbling), In Proceedings of The International Joint Conference on Autonomous Agents and Multi Agent Systems, 2012. BIBTEXPDF
[3] Influence-Based Abstraction for Multiagent Systems (Frans A. Oliehoek and Stefan Witwicki andLeslie P. Kaelbling), In Proceedings of the National Conference on Artificial Intelligence, 2012. BIBTEXPDF
[2] Tree-Based Solution Methods for Multiagent POMDPswith Delayed Communication (Frans A. Oliehoek and Matthijs T. J. Spaan), In Proceedings of the National Conference on Artificial Intelligence, 2012. BIBTEXPDF
[1] Scaling Up Decentralized MDPs Through Heuristic Search (Jilles S. Dibangoye and Christopher Amato and Arnaud Doniec), In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012. BIBTEXPDF
[1] Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion (Matthijs T. J. Spaan and Frans A. Oliehoek and Christopher Amato), In Proceedings of the International Joint Conference on Artificial Intelligence, 2011. BIBTEXPDF
[1] All learning is Local: Multi-agent Learning in Global Reward Games ( Yu-Han Chang and Tracey Ho and Leslie Pack Kaelbling), In Advances in Neural Information Processing Systems 16, 2004. BIBTEXPDF
[1] Learning to Cooperate via Policy Search (Leonid Peshkin and Kee-Eung Kim and Nicolas Meuleau and Leslie Pack Kaelbling), In Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-2000), 2000. BIBTEXPDF

// (PO)MDP

  • 2019
  • 2018
  • 2017
  • 2016
  • 2015
  • 2014
  • 2013
  • 2012
  • 2011
  • 2010
  • 2004
  • 2002
  • 1998
  • 1997
[1] Modeling and Planning with Macro-Actions in Decentralized POMDPs (Christopher Amato and George Konidaris and Leslie Pack Kaelbling and Jonathan P. How), In Journal of Artificial Intelligence Research, volume 64, 2019. BIBTEXPDF
[2] Learning What Information to Give in Partially Observed Domains (Rohan Chitnis and Leslie Pack Kaelbling and Tomas Lozano-Perez), In Conference on Robot Learning (CoRL), 2018. BIBTEXPDF
[1] Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization (Rohan Chitnis and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Conference on Intelligent Robots and Systems (IROS), 2018. BIBTEXPDF
[1] Policy search for multi-robot coordination under uncertainty (Christopher Amato and George Konidaris and Ariel Anders and Gabriel Cruz and Jonathan P How and Leslie P Kaelbling), In The International Journal of Robotics Research, volume 35, 2017. BIBTEXPDF
[2] Bounded Optimal Exploration in MDP (Kenji Kawaguchi), In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016. BIBTEXPDF
[1] Bounded Optimal Exploration in MDP (Kenji Kawaguchi), In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI), 2016. BIBTEXPDF
[2] Planning for Decentralized Control of Multiple Robots Under Uncertainty (C. Amato and G.D. Konidaris and G. Cruz and C. Maynor and J.P. How and L.P. Kaelbling), In Proceedings of the 2015 IEEE International Conference on Robotics and Automation, 2015. BIBTEXPDF
[1] Policy Search for Multi-Robot Coordination under Uncertainty (C. Amato and G.D. Konidaris and A. Anders and G. Cruz and J.P. How and L.P. Kaelbling), In Robotics: Science and Systems XI (RSS), 2015. BIBTEXPDF
[2] Planning with Macro-Actions in Decentralized POMDPs (Christopher Amato and George Konidaris and Leslie Pack Kaelbling), In Thirteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-14), 2014. BIBTEXPDF
[1] Exploiting Separability in Multi-Agent Planning with Continuous-State MDPs. (Jilles S. Dibangoye and Christopher Amato and Olivier Buffet and Francois Charpillet), In Thirteenth International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS-14), 2014. BIBTEXPDF
[2] Producing Efficient Error-bounded Solutions for Transition Independent Decentralized MDPs (Jilles S. Dibangoye and Christopher Amato and Arnaud Doniec and Francois Charpillet), In Proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems, 2013. BIBTEXPDF
[1] Optimally Solving Dec-POMDPs as Continuous-State MDPs (Jilles S. Dibangoye and Christopher Amato and Olivier Buffet and Francois Charpillet), In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, 2013. BIBTEXPDF
[2] Diagnose and Decide: An Optimal Bayesian Approach (Christopher Amato and Emma Brunskill), In Proceedings of the Workshop on Bayesian Optimization and Decision Making at NIPS 2012, 2012. BIBTEXPDF
[1] Scaling Up Decentralized MDPs Through Heuristic Search (Jilles S. Dibangoye and Christopher Amato and Arnaud Doniec), In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012. BIBTEXPDF
[3] Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion (Matthijs T. J. Spaan and Frans A. Oliehoek and Christopher Amato), In Proceedings of the International Joint Conference on Artificial Intelligence, 2011. BIBTEXPDF
[2] DetH*: approximate Hierarchical Solution of Large Markov Decision Processes (Jennifer L. Barry and Leslie Pack Kaelbling and Tomas Lozano-Perez), In International Joint Conference on Artificial Intelligence (IJCAI), 2011. BIBTEXPDF
[1] Bayesian Policy Search with Policy Priors (David Wingate and Noah D. Goodman and Daniel M. Roy and Leslie P. Kaelbling and Joshua B. Tenenbaum ), In International Joint Conference on Artificial Intelligence (IJCAI), 2011. BIBTEXPDF
[1] Planning in partially observable switching-mode continuous domains (Emma Brunskill and Leslie Pack Kaelbling and Tomas Lozano-Perez and Nicholas Roy), In Annals of Mathematics and Artificial Intelligence, 2010. BIBTEXPDF
[2] Envelope-based Planning in Relational MDPs (Natalia H. Gardiol and Leslie Pack Kaelbling), In NIPS, volume 16, 2004. BIBTEXPDF
[1] Approximate Planning in POMDPs with Macro-Actions (Georgios Theocharous and Leslie Pack Kaelbling), In Advances in Neural Information Processing Systems 16 (NIPS03), 2004. BIBTEXPDF
[1] Nearly deterministic abstractions of Markov decision processes (Terran Lane and Leslie Pack Kaelbling), In 18th National Conference on Artificial Intelligence, 2002. BIBTEXPDF
[1] A Framework for Multiple Instance Learning (Oded Maron and Tomas Lozano-Perez), In Advances in Neural Information Processing Systems 10, 1998. BIBTEXPDF
[1] Solving the multiple instance problem with axis-parallel rectangles (Thomas G. Dietterich and Richard H. Lathrop and Tomas Lozano-Perez), In Artificial Intelligence, volume 89, 1997. BIBTEXPDF

// Transfer Learning

  • 2019
  • 2018
  • 2012
  • 2007
  • 2005
[3] Neural Relational Inference with Fast Modular Meta-learning (Alet, Ferran and Weng, Erica and Lozano-Perez, Tomas and Kaelbling, Leslie Pack), , 2019. BIBTEXPDF
[2] Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video (Maria Bauza and Ferran Alet and Yen-Chen Lin and Tomas Lozano-Perez and Leslie P. Kaelbling and Phillip Isola and Alberto Rodriguez), In International Conference on Intelligent Robots and Systems (IROS), 2019. BIBTEXPDF
[1] Graph Element Networks: adaptive, structured computation and memory (Alet, Ferran and Jeewajee, Adarsh Keshav and Villalonga, Maria Bauza and Rodriguez, Alberto and Lozano-Perez, Tomas and Kaelbling, Leslie), In Proceedings of the 36th International Conference on Machine Learning, volume 97, 2019. BIBTEXPDF
[1] Modular meta-learning (Ferran Alet, Tomas Lozano-Perez, Leslie Pack Kaelbling), In Conference on Robot Learning (CoRL), 2018. BIBTEXPDF
[1] Transfer Learning by Discovering Latent Task Parametrizations (F. Doshi-Velez and G.D. Konidaris), In NIPS 2012 Workshop on Bayesian Nonparametric Models for Reliable Planning And Decision-Making Under Uncertainty, 2012. BIBTEXPDF
[2] Learning Probabilistic Relational Dynamics for Multiple Tasks (Ashwin Deshpande and Brian Milch and Luke S. Zettlemoyer and LesliePack Kaelbling), In Conference on Uncertainty in Artificial Intelligence (UAI), 2007. BIBTEXPDF
[1] Efficient Bayesian Task-level Transfer Learning (Daniel M. Roy and Leslie Pack Kaelbling), In International Joint Conference on Artificial Intelligence (IJCAI), 2007. BIBTEXPDF
[1] Transfer Learning with an Ensemble of Background Tasks (Michael Rosenstein and Zvika Marx and Tom Dietterichand Leslie Pack Kaelbling), In NIPS Workshop on Inductive Transfer, 2005. BIBTEXPDF

// Grasping

  • 2019
  • 2011
  • 2010
  • 2008
  • 2007
  • 2006
[1] Force-And-Motion Constrained Planning for Tool Use (Rachel Holladay and Tomas Lozano-Perez and Alberto Rodriguez), In International Conference on Intelligent Robots and Systems (IROS), 2019. BIBTEXPDF
[3] Robust grasping under object pose uncertainty (Kaijen Hsiao and Leslie Pack Kaelbling and Tomas Lozano-Perez), In Autonomous Robots, volume 31, 2011. BIBTEXPDF
[2] Efficient planning in non-Gaussian belief spaces and its application to robot grasping (Robert Platt and Leslie Pack Kaelbling and Tomas Lozano-Perez and Russ Tedrake), In International Symposium on Robotics Research (ISRR), 2011. BIBTEXPDF
[1] Efficient planning in non-Gaussian belief spaces and its application to robot grasping (Robert Platt and Leslie Pack Kaelbling and Tomas Lozano-Perez and Russ Tedrake), In International Symposium on Robotics Research (ISRR), 2011. BIBTEXPDF
[1] Task-driven Tactile Exploration (Kaijen Hsiao and Leslie Pack Kaelbling and Tomas Lozano-Perez), In Robotics Science and Systems (RSS), 2010. BIBTEXPDF
[1] Robust Belief-Based Execution of Manipulation Programs (Kaijen Hsiao and Tomas Lozano-Perez and Leslie Pack Kaelbling), In Eighth International Workshop on the Algorithmic Foundations of Robotics (WAFR), 2008. BIBTEXPDF
[1] Grasping POMDPs (Kaijen Hsiao and Leslie Pack Kaelbling and Tomas Lozano-Perez), In IEEE International Conference on Robotics and Automation, 2007. BIBTEXPDF
[1] Imitation Learning of Whole-Body Grasps (Kaijen Hsiao and Tomas Lozano-Perez), In IEEE/RSJ Intl. Conf. on Intelligent Robots and Systems (IROS), 2006. BIBTEXPDF

// Object Recognition

  • 2013
  • 2011
  • 2009
  • 2007
[1] Bingham Procrustean Alignment for Object Detection in Clutter (Jared Glover and Sanja Popovic), In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013. BIBTEXPDF
[1] Monte Carlo Pose Estimation with Quaternion Kernels and the Bingham Distribution (J. Glover and G. Bradksi and R. Rusu), In Proceedings of Robotics: Science and Systems, 2011. BIBTEXPDF
[1] Learning to Generate Novel Views of Objects for Class Recognition. (Hang-Pan Chiu and Leslie P. Kaelbling andTomas Lozano-Perez), In Computer Vision and Image Understanding, 2009. BIBTEXPDF
[1] Virtual Training for Multi-View Object Class Recognition. (Hang-Pan Chiu and Leslie Pack Kaelbling andTomas Lozano-Perez), In CVPR, 2007. BIBTEXPDF

// Game AI

  • 2012
  • 2011
[1] POMCoP: Belief Space Planning for Sidekicks in Cooperative Games (O. Macindoe and L. P. Kaelbling and T. Lozano-Perez), In Proceedings of the 8th Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2012. BIBTEXPDF
[1] CAPIR: Collaborative Action Planning with Intention Recognition (Truong-Huy Dinh Nguyen and David Hsu and Wee-Sun Lee and Tze-Yun Leong and Leslie Pack Kaelbling and Tomas Lozano-Perez and Andrew Haydn Grant), In Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), 2011. BIBTEXPDF

// Computational Biology

  • 2006
  • 2002
  • 1998
[1] Protein side-chain placement through MAP estimation andproblem-size reduction (Eun-Jong Hong and Tomas Lozano-Perez), In 6th Workshop on Algorithms in Bioinformatics(WABI), 2006. BIBTEXPDF
[1] De novo determination of peptide structure with solid-state magic-angle spinning NMR spectroscopy. (C. M. Rienstra and L. Tucker-Kellogg and C. P. Jaroniec and M. Hohwy and B. Reif and M. T. McMahon and B. Tidor and T. Lozano-Perez and R. G. Griffin), In Proceedings of the National Academy of Sciences, USA, volume 99, 2002. BIBTEXPDF
[1] AmbiPack: a systematic algorithm for packing of macromolecular structures withambiguous distance constraints (C.S. Wang and T. Lozano-Perez and B. Tidor ), In Proteins, volume 32, 1998. BIBTEXPDF