L. Kaelbling

Leslie Pack Kaelbling, James Kurien, Anthony R. Cassandra, 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

Representing hierarchical POMDPs as DBNs for multi-scale robot localization

Leslie Pack Kaelbling, Kevin P. Murphy, Georgios Theocharous, 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

On reinforcement learning for robots

Leslie Pack Kaelbling, L. Kaelbling, 1996, IROS.

Associative reinforcement learning: A generate and test algorithm

L. Kaelbling, 2004, Machine Learning.

Hierarchical Learning in Stochastic Domains: Preliminary Results

Leslie Pack Kaelbling, L. Kaelbling, 1993, ICML.

Practical Reinforcement Learning in Continuous Spaces

Leslie Pack Kaelbling, William D. Smart, W. Smart, 2000, ICML.

Learning Policies with External Memory

Leslie Pack Kaelbling, Nicolas Meuleau, Leonid Peshkin, 1999, ICML.

Acting Optimally in Partially Observable Stochastic Domains

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1994, AAAI.

Hierarchical Solution of Markov Decision Processes using Macro-actions pdf

Milos Hauskrecht, Craig Boutilier, Leslie Pack Kaelbling, 1998, UAI.

Belief space planning assuming maximum likelihood observations

Leslie Pack Kaelbling, Robert Platt, Tomás Lozano-Pérez, 2010, Robotics: Science and Systems.

Effective reinforcement learning for mobile robots

Leslie Pack Kaelbling, William D. Smart, W. Smart, 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

Making Reinforcement Learning Work on Real Robots

Leslie Pack Kaelbling, William D. Smart, W. Smart, 2002 .

Practical Reinforcement Learning

W. Smart, L. Kaelbling, 2000, ICML 2000.

Adaptive Importance Sampling for Estimation in Structured Domains

Leslie Pack Kaelbling, Luis E. Ortiz, L. Kaelbling, 2000, UAI.

Planning under Time Constraints in Stochastic Domains

Leslie Pack Kaelbling, Ann E. Nicholson, Thomas L. Dean, 1993, Artif. Intell..

Planning With Deadlines in Stochastic Domains

Leslie Pack Kaelbling, Ann E. Nicholson, Thomas L. Dean, 1993, AAAI.

Solving POMDPs by Searching the Space of Finite Policies

Kee-Eung Kim, Leslie Pack Kaelbling, Nicolas Meuleau, 1999, UAI.

Generalization in Deep Learning pdf

Leslie Pack Kaelbling, Yoshua Bengio, Kenji Kawaguchi, 2017, ArXiv.

Continuous-State POMDPs with Hybrid Dynamics

Leslie Pack Kaelbling, Nicholas Roy, Tomás Lozano-Pérez, 2008, ISAIM.

Backward-Forward Search for Manipulation Planning Completeness Argument

Caelan Reed Garrett, L. Kaelbling, Tomas Lozano-Perez, 2015 .

Reinforcement Learning by Policy Search

Leslie Pack Kaelbling, Leonid Peshkin, L. Kaelbling, 2002 .

Selecting approximately-optimal actions in complex structured domains

Luis E. Ortiz, L. Kaelbling, 2002 .

The Thing that we Tried Didn't Work very Well: Deictic Representation in Reinforcement Learning

Tim Oates, Leslie Pack Kaelbling, Sarah Finney, 2002, UAI.

Learning with Deictic Representations

L. Kaelbling, S. Finney, N. Gardiol, 2001 .

Hierarchical task and motion planning in the now

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2011, 2011 IEEE International Conference on Robotics and Automation.

Efficient Planning in Non-Gaussian Belief Spaces and Its Application to Robot Grasping

Leslie Pack Kaelbling, Robert Platt, Tomás Lozano-Pérez, 2011, ISRR.

A hypothesis-based algorithm for planning and control in non-Gaussian belief spaces

L. Kaelbling, Russ Tedrake, Tomas Lozano-Perez, 2011 .

Integrated task and motion planning in belief space

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2013, Int. J. Robotics Res..

Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs

Andrew W. Moore, Leslie Pack Kaelbling, Leemon C. Baird, 1999, IJCAI.

All learning is Local: Multi-agent Learning in Global Reward Games

Leslie Pack Kaelbling, Tracey Ho, Yu-Han Chang, 2003, NIPS.

Solving Very Large Weakly Coupled Markov Decision Processes

Kee-Eung Kim, Milos Hauskrecht, Craig Boutilier, 1998, AAAI/IAAI.

Learning Topological Maps with Weak Local Odometric Information

Leslie Pack Kaelbling, Hagit Shatkay, L. Kaelbling, 1997, IJCAI.

Deliberation Scheduling for Time-Critical Sequential Decision Making

Leslie Pack Kaelbling, Ann E. Nicholson, Thomas L. Dean, 1993, UAI.

The Synthesis of Digital Machines With Provable Epistemic Properties

Leslie Pack Kaelbling, Stanley J. Rosenschein, S. Rosenschein, 1986, TARK.

Recent Advances in Reinforcement Learning

Leslie Pack Kaelbling, L. Kaelbling, 1996, Springer US.

A constraint-based method for solving sequential manipulation planning problems

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Backward-forward search for manipulation planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

FFRob: An Efficient Heuristic for Task and Motion Planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2015, WAFR.

Efficient Bayesian Task-Level Transfer Learning

Leslie Pack Kaelbling, Daniel M. Roy, L. Kaelbling, 2007, IJCAI.

Hierarchical planning for multi-contact non-prehensile manipulation

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Gilwoo Lee, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Grasping POMDPs

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Kaijen Hsiao, 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

On the Complexity of Solving Markov Decision Problems pdf

Leslie Pack Kaelbling, Thomas L. Dean, Michael L. Littman, 1995, UAI.

Goals as Parallel Program Specifications

Leslie Pack Kaelbling, L. Kaelbling, 1988, AAAI.

Toward Approximate Planning in Very Large Stochastic Domains

Leslie Pack Kaelbling, Ann E. Nicholson, L. Kaelbling, 1994, AAAI 1994.

Heuristic Search for Task and Motion Planning

L. Kaelbling, 2014 .

Two Algorithms for Transfer Learning

Thomas G. Dietterich, M. Rosenstein, L. Kaelbling, .

Inferring finite automata with stochastic output functions and an application to map learning

Leslie Pack Kaelbling, Shlomo Argamon, Kenneth Basye, 1992, 26th Annual Symposium on Foundations of Computer Science (sfcs 1985).

Task-Driven Tactile Exploration

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Kaijen Hsiao, 2010, Robotics: Science and Systems.

Rex Programmer's Manual

Leslie Pack Kaelbling, Nathan J. Wilson, L. Kaelbling, 1988 .

Sampling Methods for Action Selection in Influence Diagrams

Leslie Pack Kaelbling, Luis E. Ortiz, L. Kaelbling, 2000, AAAI/IAAI.

Simultaneous Localization and Grasping as a Belief Space Control Problem

L. Kaelbling, Russ Tedrake, Tomas Lozano-Perez, 2011 .

Multi-Agent Filtering with Infinitely Nested Beliefs

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Brian Milch, 2008, NIPS.

Toward Hierachical Decomposition for Planning in Uncertain Environments

L. Kaelbling, T. Lane, 2001 .

Bayesian Optimization with Exponential Convergence

Leslie Pack Kaelbling, Kenji Kawaguchi, Tomás Lozano-Pérez, 2015, NIPS.

A Dynamical Model of Visually-Guided Steering, Obstacle Avoidance, and Route Selection

Leslie Pack Kaelbling, Brett R. Fajen, William H. Warren, 2003, International Journal of Computer Vision.

The NSF Workshop on Reinforcement Learning: Summary and Observations

Leslie Pack Kaelbling, Sridhar Mahadevan, S. Mahadevan, 1996 .

State-based Classification of Finger Gestures from Electromyographic Signals

Leslie Pack Kaelbling, Yoram Singer, Peter Ju, 2000, ICML.

Hierarchical Planning in the Now

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2010, Bridging the Gap Between Task and Motion Planning.

Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks pdf

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2020, AAAI.

CAPIR: Collaborative Action Planning with Intention Recognition pdf

Leslie Pack Kaelbling, Tomás Lozano-Pérez, David Hsu, 2011, AIIDE.

Heading in the Right Direction

Leslie Pack Kaelbling, Hagit Shatkay, L. Kaelbling, 1998, ICML.

Learning Hidden Markov Models with Geometric Information Learning Hidden Markov Models with Geometric Information

L. Kaelbling, H. Shatkay, 1997 .

Approximate Planning in POMDPs with Macro-Actions

Leslie Pack Kaelbling, Georgios Theocharous, L. Kaelbling, 2003, NIPS.

Symbol Acquisition for Probabilistic High-Level Planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, George Konidaris, 2015, IJCAI.

Reports of the AAAI 2010 Conference Workshops

Gregory M. Provan, David W. Aha, Robert P. Goldman, 2010, AI Mag..

Provably safe robot navigation with obstacle uncertainty

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Brian Axelrod, 2017, Robotics: Science and Systems.

Bayesian Policy Search with Policy Priors

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Noah D. Goodman, 2011, IJCAI.

Algorithms for multi-armed bandit problems

Leslie Pack Kaelbling, Doina Precup, Volodymyr Kuleshov, 2014, ArXiv.

Differentiable Algorithm Networks for Composable Robot Learning pdf

Leslie Pack Kaelbling, Tomás Lozano-Pérez, David Hsu, 2019, Robotics: Science and Systems.

Object placement as inverse motion planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Jennifer L. Barry, 2013, 2013 IEEE International Conference on Robotics and Automation.

Associative Reinforcement Learning: A Generate and Test Algorithm

Leslie Pack Kaelbling, L. Kaelbling, 1994, Machine Learning.

Interval programming: a multi-objective optimization model for autonomous vehicle control

Leslie Pack Kaelbling, Michael R. Benjamin, L. Kaelbling, 2002 .

Automated Design of Adaptive Controllers for Modular Robots using Reinforcement Learning

Leslie Pack Kaelbling, Daniela Rus, Paulina Varshavskaya, 2008, Int. J. Robotics Res..

On Scalability Issues in Reinforcement Learning for Self-Reconfiguring Modular Robots

L. Kaelbling, D. Rus, Paulina Varshavskaya, 2006 .

Meta-learning curiosity algorithms pdf

Leslie Pack Kaelbling, Ferran Alet, Tomas Lozano-Perez, 2020, ICLR.

Planning for decentralized control of multiple robots under uncertainty

Jonathan P. How, Leslie Pack Kaelbling, George Konidaris, 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

PLANNING UNDER UNCERTAINTY IN COMPLEX STRUCTURED ENVIRONMENTS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

L. Kaelbling, 2003 .

I Administrative Title: Adaptive Intelligent Mobile Robots

L. Kaelbling, 2022 .

Report of the 1996 Workshop on Reinforcement Learning Sponsored by the National Science Foundation Preface and Acknowledgements

S. Mahadevan, L. Kaelbling, 2007 .

Effective, interpretable algorithms for curiosity automatically discovered by evolutionary search

L. Kaelbling, Tomas Lozano-Perez, Ferran Alet, 2020 .

Representation Discovery in Non-Parametric Reinforcement Learning by Dawit Zewdie

L. Kaelbling, Dawit Zewdie, 2014 .

Learning Skill Hierarchies from Predicate Descriptions and Self-Supervision

J. Tenenbaum, L. Kaelbling, Rohan Chitnis, 2019 .

Search for Multi-Robot Coordination under Uncertainty

G. Konidaris, L. Kaelbling, J. How, 2016 .

Domain and Plan Representation for Task and Motion Planning in Uncertain Domains

L. Kaelbling, Tomas Lozano-Perez, 2011 .

2 Short-term Research Priorities 2 . 1 Optimizing AI ’ s Economic Impact

D. Hassabis, D. Parkes, L. Kaelbling, 2022 .

Title: Using Context for Human-robot Interaction Title: Using Context for Human-robot Interaction Title: Using Context for Human-robot Interaction Title: Using Context for Human-robot Interaction Title: Using Context for Human-robot Interaction Context in Human-robot Interaction

T. Kollar, L. Kaelbling, 2022 .

[4] Reinforcement Learning for Planning and Control

D. Angluin, T. Dean, L. Kaelbling, 2008 .

Combining dynamic abstractions in very large MDPs

L. Kaelbling, Kurt Steinkraus, 2004 .

Representing hierarchical POMDPs as DBNs for multi-scale map learning

K. Murphy, L. Kaelbling, Georgios Theocharous, 2003 .

Time-Critical Planning and Scheduling Research atBrown

Lloyd G. Greenwald, L. Kaelbling, J. Kirman, 2008 .

PG3: Policy-Guided Planning for Generalized Policy Generation

L. Kaelbling, Tomas Lozano-Perez, Tom Silver, 2022, IJCAI.

Model-based Monitoring, Diagnosis and Control

T. Dean, L. Kaelbling, J. Kurien, 2003 .

Engine s " s " # $ Learned Physics Engine s " s " # $ Learned Physics Engine s " s " # $ Learned Physics Engine s "

J. Tenenbaum, L. Kaelbling, Maria Bauzá, 2018 .

OmniPush : accurate , diverse , real-world dataset of pushing dynamics with RGBD images

Tomás Lozano-Pérez, Ferran Alet, Maria Bauza, 2018 .

LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics

Leslie Pack Kaelbling, Robert Platt, Alejandro Perez, 2012, 2012 IEEE International Conference on Robotics and Automation.

Planning Wit stic s

L. Kaelbling, A. Nicholson, 1993 .

Planning under Time Constraints in Stochastic Domains Planning under Time Constraints in Stochastic Domains

Leslie Pack Kaelbling, L. Kaelbling, 1993 .

Learning in Average Reward Stochastic Games A Reinforcement Learning (Nash-R) Algorithm for Average Reward Irreducible Stochastic Games

L. Kaelbling, T. Das, K. Ramachandran, 2013 .

Appendix for Monte Carlo Tree Search in high-dimensional continuous spaces using Voronoi optimistic optimization with regret bounds

L. Kaelbling, Tomas Lozano-Perez, Beomjoon Kim, 2020 .

Time-Critical Planning and Scheduling inStochastic Domains ( Extended Abstract )

Lloyd G. Greenwald, T. Dean, L. Kaelbling, 2007 .

Provably safe robot navigation with obstacle uncertainty

Leslie Pack Kaelbling, Brian Axelrod, Tomas Lozano-Perez, 2017, Robotics: Science and Systems.

Computing action equivalences for planning

L. Kaelbling, N. Gardiol, 2006 .

Planning with Probabilistic Rules in a Relational World

L. Kaelbling, N. Gardiol, 2002 .

ATC-356 Unmanned Aircraft Collision Avoidance Using Partially Observable Markov Decision Processes

Mykel J. Kochenderfer, L. Kaelbling, Tomas Lozano-Perez, 2009 .

Spatial and Temporal Abstractions in POMDPS : Learning and Planning

L. Kaelbling, Georgios Theocharous, 2004 .

Associative methods in reinforcement learning: an empirical study

Leslie Pack Kaelbling, L. Kaelbling, 1994, COLT 1994.

Mobilized ad-hoc networks: a reinforcement learning approach

Leslie Pack Kaelbling, Tracey Ho, Yu-Han Chang, 2004, International Conference on Autonomic Computing, 2004. Proceedings..

Probabilistic Planning for Multi-Robot Systems

G. Konidaris, L. Kaelbling, J. How, 2022 .

Decentralized Decision-Making Under Uncertainty for Multi-Robot Teams

G. Konidaris, L. Kaelbling, J. How, 2014 .

Reasoning about Large Populations with Lifted Probabilistic Inference

Leslie Pack Kaelbling, Kristian Kersting, Luke Zettlemoyer, 2007, NIPS 2007.

Planning with Macro-Actions in Decentralized POMDPs Citation

G. Konidaris, L. Kaelbling, Chris Amato, 2014 .

Learning structured transition models for multi-object manipulation

L. Kaelbling, Zi Wang, Victoria Xia, 2018 .

N 90-29077 Integrating Planning and Reactive

S. Rosenschein, L. Kaelbling, 2022 .

Automatic Synthesis of Rules for Planning in Belief Space

L. Kaelbling, 2013 .

Learning Topological Maps from Weak Odometric Information

Leslie Pack Kaelbling, Hagit Shatkay, L. Kaelbling, 1997, IJCAI 1997.

A Sufficient Statistic for Influence in Structured Multiagent Environments pdf

Frans A. Oliehoek, Stefan Witwicki, Leslie P. Kaelbling, 2019, J. Artif. Intell. Res..

Learning Planning Rules in Stochastic Worlds

Leslie Pack Kaelbling, Luke Zettlemoyer, Hanna Pasula, 2005, AAAI 2005.

Action 35 and Planning in Embedded Agents *

S. Rosenschein, L. Kaelbling, 2013 .

Statistical Relational Artificial Intelligence, Papers from the 2010 AAAI Workshop, Atlanta, Georgia, USA, July 12, 2010

Leslie Pack Kaelbling, Kristian Kersting, Sriraam Natarajan, 2010, Statistical Relational Artificial Intelligence.

Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Geometrical-Topological Gap

Leslie Pack Kaelbling, Hagit Shatkay, L. Kaelbling, 2002 .

Learning Neuro-Symbolic Skills for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, ArXiv.

Fully Persistent Spatial Data Structures for Efficient Queries in Path-Dependent Motion Planning Applications

L. Kaelbling, Tomas Lozano-Perez, S. Karnik, 2022, 2022 International Conference on Robotics and Automation (ICRA).

Towards Understanding Generalization via Analytical Learning Theory

Yoshua Bengio, L. Kaelbling, Kenji Kawaguchi, 2018, 1802.07426.

Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2021, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Inferring Finite Automata with Stochastic Output Functions and an Application to Map Learning

D. Angluin, T. Dean, L. Kaelbling, 1992, Machine Learning.

6.034 Artificial Intelligence, Spring 2003

Leslie Pack Kaelbling, Patrick Henry Winston, Tomas Lozano-Perez, 2003 .

Efficient dynamic-programming updates in partially observable Markov decision processes

M. Littman, A. Cassandra, L. Kaelbling, 1995 .

Finding aircraft collision-avoidance strategies using policy search methods

Leslie Pack Kaelbling, Tomas Lozano-Perez, L. Kaelbling, 2009 .

Learning Symbolic Operators for Task and Motion Planning

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Learning to Plan with Optimistic Action Models

L. Kaelbling, Tomas Lozano-Perez, Caris Moses, 2022 .

Learning Rich , Tractable Models of the Real World

L. Kaelbling, 1999 .

FFRob: An eﬃcient heuristic for task and motion planning

Caelan Reed Garrett, L. Kaelbling, Tomas Lozano-Perez, 2014 .

Probabilistic Planning for Decentralized Multi-Robot Systems

Jonathan P. How, Leslie Pack Kaelbling, George Konidaris, 2015, AAAI Fall Symposia.

Learning Neuro-Symbolic Skills for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, ArXiv.

HYPERGRAPH REASONING NETWORKS

L. Kaelbling, 2021 .

Learning in embedded systems

L. Kaelbling, 1993 .

A Bibliography of Work Related to Reinforcement Learning

Leslie Pack Kaelbling, Michael L. Littman, M. Littman, 1994 .

An Efficient Algorithm for Dynamic Programming in Partially Observable Markov Decision Processes

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1995 .

Algorithms for Partially Observable Markov Decision Processes

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1994 .

Learning Hierarchical Structure in Policies

Leslie Pack Kaelbling, Bhaskara Marthi, Tomas Lozano-Perez, 2007, NIPS 2007.

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

Caelan Reed Garrett, D. Fox, L. Kaelbling, 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Learning Operators with Ignore Effects for Bilevel Planning in Continuous Domains

L. Kaelbling, Tomas Lozano-Perez, Rohan Chitnis, 2022, ArXiv.

Learning Neuro-Symbolic Skills for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, CoRL.

Predicate Invention for Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, AAAI.

Integrated Agent Architectures: Benchmark Tasks and Evaluation Metrics

Leslie Pack Kaelbling, Mark E. Drummond, L. Kaelbling, 1990 .

Simultaneous Localization and Grasping as a Belief Space Control Problem

L. Kaelbling, Russ Tedrake, Tomas Lozano-Perez, 2011 .

Learning object boundary detection from motion data

L. Kaelbling, Michael G. Ross, 2003 .

Learning Object-Based State Estimators for Household Robots

L. Kaelbling, Tomas Lozano-Perez, Yilun Du, 2020, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Task-Directed Exploration in Continuous POMDPs for Robotic Manipulation of Articulated Objects

L. Kaelbling, Siddarth Jain, Aidan Curtis, 2022, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Lifted Probabilistic Inference with Counting Formulas

Leslie Pack Kaelbling, Kristian Kersting, Luke S. Zettlemoyer, 2008, AAAI.

Hedged learning: regret-minimization with learning experts

Leslie Pack Kaelbling, Yu-Han Chang, L. Kaelbling, 2005, ICML.

Playing is believing: The role of beliefs in multi-agent learning

Leslie Pack Kaelbling, Yu-Han Chang, L. Kaelbling, 2001, NIPS.

Focused model-learning and planning for non-Gaussian continuous state-action systems

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Stefanie Jegelka, 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Relational envelope-based planning

Leslie Pack Kaelbling, Natalia H. Gardiol, L. Kaelbling, 2008 .

Computing action equivalences for planning under time-constraints

Leslie Pack Kaelbling, Natalia H. Gardiol, L. Kaelbling, 2006 .

Revised submission to Artificial Intelligence special issue on Planning and Scheduling

L. Kaelbling, J. Kirman, 1995 .

A Situated View of Representation and Control

Leslie Pack Kaelbling, Stanley J. Rosenschein, S. Rosenschein, 1995, Artif. Intell..

Modeling and Planning with Macro-Actions in Decentralized POMDPs

Jonathan P. How, Leslie Pack Kaelbling, George Konidaris, 2019, J. Artif. Intell. Res..

Planning with macro-actions in decentralized POMDPs

Leslie Pack Kaelbling, George Konidaris, Christopher Amato, 2014, AAMAS.

Planning with Macro-Actions in Decentralized POMDPs Citation

G. Konidaris, L. Kaelbling, Chris Amato, 2014 .

Influence-Based Abstraction for Multiagent Systems

Leslie Pack Kaelbling, Frans A. Oliehoek, Stefan J. Witwicki, 2012, AAAI.

Learning in Worlds with Objects

Tim Oates, Leslie Pack Kaelbling, Sarah Finney, 2017, Encyclopedia of Machine Learning and Data Mining.

Learning to Acquire Information pdf

Leslie Pack Kaelbling, Armando Solar-Lezama, Yewen Pu, 2017, UAI.

Effect of Depth and Width on Local Minima in Deep Learning

Leslie Pack Kaelbling, Kenji Kawaguchi, Jiaoyang Huang, 2018, Neural Computation.

Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap pdf

L. P. Kaelbling, H. Shatkay, L. Kaelbling, 2011, J. Artif. Intell. Res..

Every Local Minimum Value Is the Global Minimum Value of Induced Model in Nonconvex Machine Learning

Kenji Kawaguchi, Jiaoyang Huang, Leslie Pack Kaelbling, 2019, Neural Computation.

Learning to generate novel views of objects for class recognition

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Han-Pang Chiu, 2009, Comput. Vis. Image Underst..

Non-Gaussian belief space planning: Correctness and complexity

Leslie Pack Kaelbling, Robert Platt, Tomás Lozano-Pérez, 2012, 2012 IEEE International Conference on Robotics and Automation.

Object-Based World Modeling in Semi-Static Environments with Dependent Dirichlet Process Mixtures

Leslie Pack Kaelbling, Lawson L. S. Wong, Tomás Lozano-Pérez, 2015, IJCAI.

Data association for semantic world modeling from partial views

Leslie Pack Kaelbling, Lawson L. S. Wong, Tomás Lozano-Pérez, 2015, Int. J. Robotics Res..

Constructing Semantic World Models from Partial Views

Leslie Pack Kaelbling, Lawson L. S. Wong, Tomas Lozano-Perez, 2013, RSS 2013.

Submitted to Artificial Intelligence Special Issue on Planning and Scheduling Planning under Time Constraints in Stochastic Domains

L. Kaelbling, J. Kirman, 1993 .

Specifying and achieving goals in open uncertain robot-manipulation domains pdf

Leslie Pack Kaelbling, Tom'as Lozano-P'erez, Alex LaGrassa, 2021, ArXiv.

Integrated robot task and motion planning in belief space

Leslie Pack Kaelbling, Tomas Lozano-Perez, L. Kaelbling, 2012 .

Integrated Task and Motion Planning pdf

Leslie Pack Kaelbling, Rachel Holladay, Tom Silver, 2020, Annu. Rev. Control. Robotics Auton. Syst..

PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

Caelan Reed Garrett, L. Kaelbling, Tomas Lozano-Perez, 2018, ICAPS.

FFRob: Leveraging symbolic planning for efficient task and motion planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2016, Int. J. Robotics Res..

Adaptive Envelope MDPs for Relational Equivalence-based Planning

Leslie Pack Kaelbling, Natalia H. Gardiol, L. Kaelbling, 2008 .

Constructing Symbolic Representations for High-Level Planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, George Konidaris, 2014, AAAI.

Symbol Acquisition for Task-Level Planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, George Konidaris, 2013, AAAI Workshop: Learning Rich Representations from Low-Level Sensors.

Learning Dynamics: System Identification for Perceptually Challenged Agents

Leslie Pack Kaelbling, Kenneth Basye, Thomas L. Dean, 1995, Artif. Intell..

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

Leslie Pack Kaelbling, Anurag Ajay, Jiajun Wu, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Reinforcement Learning for Classical Planning: Viewing Heuristics as Dense Reward Generators

L. Kaelbling, Michael Katz, Rohan Chitnis, 2021, ICAPS.

Few-Shot Bayesian Imitation Learning with Logical Program Policies

Kelsey R. Allen, Alexander K. Lew, J. Tenenbaum, 2019, AAAI.

A situated-automata approach to the design of embedded agents

Leslie Pack Kaelbling, L. Kaelbling, 1991, SGAR.

Specifying Complex Behavior for Computer Agents

Leslie Pack Kaelbling, L. Kaelbling, 1991 .

and Planning in Embedded Agents

Leslie Pack Kaelbling, Stanley J. Rosenschein, S. Rosenschein, 1990 .

Artificial intelligence and robotics

Leslie Pack Kaelbling, L. Kaelbling, 1988, Digest of Papers. COMPCON Spring 88 Thirty-Third IEEE Computer Society International Conference.

Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior

Leslie Pack Kaelbling, Zi Wang, Beomjoon Kim, 2018, NeurIPS.

Ecological Robotics

Leslie Pack Kaelbling, William H. Warren, Andrew P. Duchon, 1998, Adapt. Behav..

The foundation of efficient robot learning

Leslie Pack Kaelbling, L. Kaelbling, 2020, Science.

Modular meta-learning pdf

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Ferran Alet, 2018, CoRL.

Fragmented Spatial Maps: State Abstraction and Efficient Planning from Surprisal

L. Kaelbling, Tomas Lozano-Perez, I. Fiete, 2021 .

Associative Reinforcement Learning: Functions in k-DNF

Leslie Pack Kaelbling, L. Kaelbling, 1994, Machine Learning.

GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2020, AAAI.

GLIB: Exploration via Goal-Literal Babbling for Lifted Operator Learning pdf

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2020, ArXiv.

Residual Policy Learning pdf

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Tom Silver, 2018, ArXiv.

Partially Observable Markov Decision Processes for Artificial Intelligence

Leslie Pack Kaelbling, Michael L. Littman, Anthony R. Cassandra, 1995, Reasoning with Uncertainty in Robotics.

Assistant Agents for Sequential Planning Problems

Leslie Pack Kaelbling, Tomas Lozano-Perez, Owen Macindoe, 2012, Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment.

Learning What Information to Give in Partially Observed Domains

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Rohan Chitnis, 2018, CoRL.

Planning to Give Information in Partially Observed Domains with a Learned Weighted Entropy Model pdf

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Rohan Chitnis, 2018, ArXiv.

Integrating Human-Provided Information into Belief State Representation Using Dynamic Factorization

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Rohan Chitnis, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Implicit belief-space pre-images for hierarchical planning and execution

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Searching for physical objects in partially known environments

Leslie Pack Kaelbling, Lawson L. S. Wong, Xinkun Nie, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Policy search for multi-robot coordination under uncertainty

Jonathan P. How, Leslie Pack Kaelbling, George Konidaris, 2015, Int. J. Robotics Res..

Decentralized Decision-Making Under Uncertainty for Multi-Robot Teams

G. Konidaris, L. Kaelbling, J. How, 2014 .

POMCoP: Belief Space Planning for Sidekicks in Cooperative Games

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Owen Macindoe, 2012, AIIDE.

Heuristic search of multiagent influence space

Leslie Pack Kaelbling, Frans A. Oliehoek, Stefan J. Witwicki, 2012, AAMAS.

Unifying perception, estimation and action for mobile manipulation via belief space planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2012, 2012 IEEE International Conference on Robotics and Automation.

Robust grasping under object pose uncertainty

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Kaijen Hsiao, 2011, Auton. Robots.

Heuristic Search of Multiagent Influence Space Citation

L. Kaelbling, F. Oliehoek, S. Witwicki, 2011 .

Pre-image Backchaining in Belief Space for Mobile Manipulation

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2011, ISRR.

Collision Avoidance for Unmanned Aircraft using Markov Decision Processes

Leslie Pack Kaelbling, Mykel J. Kochenderfer, Tomas Lozano-Perez, 2010 .

Planning in partially-observable switching-mode continuous domains

Leslie Pack Kaelbling, Nicholas Roy, Tomás Lozano-Pérez, 2010, Annals of Mathematics and Artificial Intelligence.

Model-Based Optimization of Airborne Collision Avoidance Logic

Leslie Pack Kaelbling, Mykel J. Kochenderfer, Tomas Lozano-Perez, 2010 .

Robust Belief-Based Execution of Manipulation Programs

Leslie Pack Kaelbling, Tomas Lozano-Perez, Kaijen Hsiao, 2008 .

SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields

L. Kaelbling, Tomas Lozano-Perez, Pulkit Agrawal, 2022, CoRL.

Accelerating EM: An Empirical Study pdf

Leslie Pack Kaelbling, Luis E. Ortiz, L. Kaelbling, 1999, UAI.

IJCAI-05, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, July 30 - August 5, 2005

Leslie Pack Kaelbling, Alessandro Saffiotti, L. Kaelbling, 2005, International Joint Conference on Artificial Intelligence.

Intelligent Robots in an Uncertain World

Leslie Pack Kaelbling, L. Kaelbling, 2017, Conference on Uncertainty in Artificial Intelligence.

Transfer Learning with an Ensemble of Background Tasks

Thomas G. Dietterich, Leslie Pack Kaelbling, Zvika Marx, 2005, NIPS 2005.

Learning to guide task and motion planning using score-space representation

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Beomjoon Kim, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Learning compositional models of robot skills for task and motion planning

Caelan Reed Garrett, L. Kaelbling, Tomas Lozano-Perez, 2020, Int. J. Robotics Res..

Learning sparse relational transition models pdf

Victoria Xia, Zi Wang, Leslie Pack Kaelbling, 2018, ICLR.

From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, George Konidaris, 2018, J. Artif. Intell. Res..

Learning Planning Rules in Noisy Stochastic Worlds

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Hanna M. Pasula, 2005, AAAI.

Weighted geometric grammars for object detection in context

Leslie Pack Kaelbling, Tomas Lozano-Perez, Margaret Aycinena Lippow, 2010 .

Segmentation According to Natural Examples: Learning Static Segmentation from Motion Segmentation

Leslie Pack Kaelbling, Michael G. Ross, L. Kaelbling, 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Grammatical Models for Object Recognition

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Margaret Aycinena Lippow, 2008, Logic and Probability for Scene Interpretation.

Tracking the spin on a ping pong ball with the quaternion Bingham filter

Leslie Pack Kaelbling, Jared Glover, L. Kaelbling, 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

Tracking 3-D Rotations with the Quaternion Bingham Filter

Leslie Pack Kaelbling, Jared Glover, L. Kaelbling, 2013 .

Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time

L. Kaelbling, Tomas Lozano-Perez, Ferran Alet, 2020, NeurIPS.

Learning Three-Dimensional Shape Models for Sketch Recognition

Leslie Pack Kaelbling, Tomas Lozano-Perez, L. Kaelbling, 2005 .

Generalized Planning in PDDL Domains with Pretrained Large Language Models

J. Tenenbaum, L. Kaelbling, Michael Katz, 2023, AAAI.

Discovering State and Action Abstractions for Generalized Task and Motion Planning pdf

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Tom Silver, 2021, AAAI.

CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs pdf

Leslie Pack Kaelbling, Tom Silver, Rohan Chitnis, 2020, CoRL.

Active Model Learning and Diverse Action Sampling for Task and Motion Planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Integrated Robot Task and Motion Planning in the Now

Leslie Pack Kaelbling, Tomas Lozano-Perez, L. Kaelbling, 2012 .

STRIPStream: Integrating Symbolic Planners and Blackbox Samplers pdf

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2018, ArXiv.

Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordances pdf

Leslie Pack Kaelbling, Aidan Curtis, Caelan Reed Garrett, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation

L. Kaelbling, Tomas Lozano-Perez, Yilun Du, 2023, 2023 IEEE International Conference on Robotics and Automation (ICRA).

Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, ArXiv.

Graph Element Networks: adaptive, structured computation and memory pdf

Leslie Pack Kaelbling, Ferran Alet, Tomas Lozano-Perez, 2019, ICML.

Neural Relational Inference with Fast Modular Meta-learning

Leslie Pack Kaelbling, Ferran Alet, Tomas Lozano-Perez, 2019, NeurIPS.

Learning Quickly to Plan Quickly Using Modular Meta-Learning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Rohan Chitnis, 2018, 2019 International Conference on Robotics and Automation (ICRA).

Overcoming the Pitfalls of Prediction Error in Operator Learning for Bilevel Planning

L. Kaelbling, Tomas Lozano-Perez, Rohan Chitnis, 2022, 2208.07737.

Manipulation-based active search for occluded objects

Leslie Pack Kaelbling, Lawson L. S. Wong, Tomás Lozano-Pérez, 2013, 2013 IEEE International Conference on Robotics and Automation.

Manipulation with Multiple Action Types

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Jennifer L. Barry, 2012, ISER.

Fragmented Spatial Maps from Surprisal: State Abstraction and Efficient Planning

L. Kaelbling, Tomas Lozano-Perez, I. Fiete, 2021, bioRxiv.

Elimination of All Bad Local Minima in Deep Learning pdf

Leslie Pack Kaelbling, Kenji Kawaguchi, L. Kaelbling, 2019, AISTATS.

Envelope-based Planning in Relational MDPs

Leslie Pack Kaelbling, Natalia Hernandez-Gardiol, L. Kaelbling, 2003, NIPS.

Foundations of learning in autonomous agents

Leslie Pack Kaelbling, L. Kaelbling, 1991, Robotics Auton. Syst..

Optimization in the now: Dynamic peephole optimization for hierarchical planning

Dylan Hadfield-Menell, Leslie Pack Kaelbling, Tomás Lozano-Pérez, 2013, 2013 IEEE International Conference on Robotics and Automation.

Ecological Robotics: Controlling Behavior with Optical Flow

Leslie Pack Kaelbling, William H. Warren, Andrew P. Duchon, 1995 .

Spatial and Temporal Abstractions in POMDPs Applied to Robot Navigation

Leslie Pack Kaelbling, Sridhar Mahadevan, Georgios Theocharous, 2005 .

A large-scale benchmark for few-shot program induction and synthesis

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Ferran Alet, 2021, ICML.

Sampling-based methods for factored task and motion planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2017, Robotics: Science and Systems.

Learning Probabilistic Relational Planning Rules

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Hanna M. Pasula, 2004, ICAPS.

Logical Particle Filtering

Leslie Pack Kaelbling, Luke S. Zettlemoyer, Hanna M. Pasula, 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

Automatic Class-Specific 3D Reconstruction from a Single Image

Leslie Pack Kaelbling, Tomas Lozano-Perez, Han-Pang Chiu, 2009 .

Learning composable models of parameterized skills

Leslie Pack Kaelbling, Tomás Lozano-Pérez, L. Kaelbling, 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

Learning Static Object Segmentation from Motion Segmentation

Leslie Pack Kaelbling, Michael G. Ross, L. Kaelbling, 2005, AAAI.

Learning object segmentation from video data

Leslie Pack Kaelbling, Michael G. Ross, L. Kaelbling, 2003 .

Embodied Lifelong Learning for Task and Motion Planning

Jorge Armando Mendez Mendez, L. Kaelbling, Tomas Lozano-Perez, 2023, CoRL.

To transfer or not to transfer

Thomas G. Dietterich, M. Rosenstein, L. Kaelbling, 2005, NIPS 2005.

Representation, learning, and planning algorithms for geometric task and motion planning

L. Kaelbling, Tomas Lozano-Perez, Beomjoon Kim, 2021, Int. J. Robotics Res..

Guiding Search in Continuous State-Action Spaces by Learning an Action Sampler From Off-Target Search Experience

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Beomjoon Kim, 2018, AAAI.

Partially Observable Markov Decision Processes for Artificial Intelligence

M. Littman, A. Cassandra, L. Kaelbling, 1995, Reasoning with Uncertainty in Robotics.

Adaptable replanning with compressed linear action models for learning from demonstrations

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Clement Gehring, 2018, CoRL.

Learning distributed control for modular robots

Leslie Pack Kaelbling, Daniela Rus, Paulina Varshavskaya, 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

Sampling-based methods for factored task and motion planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2018, Int. J. Robotics Res..

Modular meta-learning in abstract graph networks for combinatorial generalization pdf

Leslie Pack Kaelbling, Maria Bauzá, Alberto Rodriguez, 2018, ArXiv.

On the Expressiveness and Generalization of Hypergraph Neural Networks

J. Tenenbaum, L. Kaelbling, Jiayuan Mao, 2023, ArXiv.

Sparse and Local Networks for Hypergraph Reasoning

L. Kaelbling, Jiayuan Mao, Guangxuan Xiao, 2023, LoG.

Finding Frequent Entities in Continuous Data

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Ferran Alet, 2018, IJCAI.

Learning Rational Subgoals from Demonstrations and Instructions

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2023, AAAI.

The National Science Foundation Workshop on Reinforcement Learning

Leslie Pack Kaelbling, Sridhar Mahadevan, S. Mahadevan, 1996, AI Mag..

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

J. Tenenbaum, L. Kaelbling, Maria Bauzá, 2018, IROS.

Few-Shot Bayesian Imitation Learning with Logic over Programs pdf

Leslie Pack Kaelbling, Joshua B. Tenenbaum, Tom Silver, 2019, ArXiv.

Combining Physical Simulators and Object-Based Networks for Control

Jiajun Wu, Nima Fazeli, Maria Bauza, 2019, 2019 International Conference on Robotics and Automation (ICRA).

Reliably Arranging Objects in Uncertain Domains

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Ariel S. Anders, 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

Learning Hidden Markov Models with Geometric Information

Leslie Pack Kaelbling, Hagit Shatkay, L. Kaelbling, 1997 .

A Framework for Reinforcement Learning on Real Robots

Leslie Pack Kaelbling, William D. Smart, W. Smart, 1998, AAAI/IAAI.

Hierarchical Solution of Large Markov Decision Processes

Leslie Pack Kaelbling, Tomas Lozano-Perez, Jennifer L. Barry, 2010 .

Nearly deterministic abstractions of Markov decision processes

Leslie Pack Kaelbling, Terran Lane, L. Kaelbling, 2002, AAAI/IAAI.

Approaches to macro decompositions of large Markov decision process planning problems

Leslie Pack Kaelbling, Terran Lane, L. Kaelbling, 2002, SPIE Optics East.

Grasping POMDPs: Theory and Experiments

Leslie Pack Kaelbling, Tomas Lozano-Perez, Kaijen Hsiao, 2007 .

A hierarchical approach to manipulation with diverse actions

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Jennifer L. Barry, 2013, 2013 IEEE International Conference on Robotics and Automation.

Learning Efficient Abstract Planning Models that Choose What to Predict

L. Kaelbling, Tomas Lozano-Perez, Rohan Chitnis, 2022, 2208.07737.

Efficient Distributed Reinforcement Learning through Agreement

Leslie Pack Kaelbling, Daniela Rus, Paulina Varshavskaya, 2008, DARS.

Automated Quantification Of Blood Microvessels In Hematoxylin And Eosin Whole Slide Images

L. Kaelbling, 2021 .

Optimizing Cascade Classifiers

Brendan McCane, Leslie Pack Kaelbling, Kevin Novins, 2022 .

Generalization in Deep Learning

Yoshua Bengio, L. Kaelbling, Kenji Kawaguchi, 2017 .

Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Kyungjae Lee, 2020, AAAI.

Generalizing Over Uncertain Dynamics for Online Trajectory Generation

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Beomjoon Kim, 2015, ISRR.

Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models

Adarsh K. Jeewajee, L. Kaelbling, 2020, NeurIPS.

Decidability of Semi-Holonomic Prehensile Task and Motion Planning

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Ashwin Deshpande, 2016, WAFR.

Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation

L. Kaelbling, Phillip Isola, Bokui (William) Shen, 2023, CoRL.

Model-Based Optimization of Airborne Collision Avoidance Logic

L. Kaelbling, J. Chryssanthacopoulos, M. Kochenderfer, 2010 .

Mobilized ad-hoc networks: a reinforcement learning approach

L. Kaelbling, Yu-Han Chang, T. Ho, 2004, International Conference on Autonomic Computing, 2004. Proceedings..

Robust Airborne Collision Avoidance through Dynamic Programming

Roland E. Weibel, L. Kaelbling, F. Oliehoek, 2011 .

Shape-Based Transfer of Generic Skills

Leslie Pack Kaelbling, Tomas Lozano-Perez, Skye Thompson, 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

Video Language Planning

Peter R. Florence, L. Kaelbling, Jonathan Tompson, 2023, ArXiv.

Instructions for Formatting JMLR Articles

Leslie Pack Kaelbling, David Cohn, L. Kaelbling, 2000 .

Use of Genetic Algorithm in Network Security

L. M. R. J. Lobo, Suhas B. Chavan, L. Kaelbling, 2012 .

DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability

Caelan Reed Garrett, D. Fox, L. Kaelbling, 2023, ArXiv.

Foresight and reconsideration in hierarchical planning and execution

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Mike Stilman, 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Modeling and Planning with Macro-Actions in Decentralized POMDPs

G. Konidaris, L. Kaelbling, J. How, 2019, Journal of Artificial Intelligence Research.

Scaling Techniques for Large Markov Decision Process Planning Problems

L. Kaelbling, T. Lane, 2001 .

Compositional Foundation Models for Hierarchical Planning

T. Jaakkola, L. Kaelbling, Pulkit Agrawal, 2023, NeurIPS.

Active Learning of Abstract Plan Feasibility pdf

Nicholas Roy, Tomás Lozano-Pérez, Leslie Kaelbling, 2021, Robotics: Science and Systems.

Compositional Generative Modeling: A Single Model is Not All You Need

L. Kaelbling, Yilun Du, 2024, ArXiv.

Compositional Diffusion-Based Continuous Constraint Solvers

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2023, CoRL.

Discovering Representative Examples for Program Synthesis

Leslie Pack Kaelbling, Armando Solar-Lezama, Zachery Miranda, 2018, ICML 2018.

Selecting Representative Examples for Program Synthesis

Leslie Pack Kaelbling, Armando Solar-Lezama, Zachery Miranda, 2017, ICML.

Learning to select examples for program synthesis pdf

Leslie Pack Kaelbling, Armando Solar-Lezama, Zachery Miranda, 2017, ArXiv.

PDSketch: Integrated Domain Programming, Learning, and Planning

J. Tenenbaum, L. Kaelbling, Tomas Lozano-Perez, 2022, NeurIPS.

Optimizing a Start-Stop Controller Using Policy Search

Leslie Pack Kaelbling, George Konidaris, Ryan McGee, 2014, AAAI.

Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video

L. Kaelbling, Tomas Lozano-Perez, Maria Bauzá, 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Predicting Partial Paths from Planning Problem Parameters

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Sarah Finney, 2007, Robotics: Science and Systems.

Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Beomjoon Kim, 2019, AAAI.

Sample-Based Methods for Factored Task and Motion Planning

Caelan Reed Garrett, L. Kaelbling, Tomás Lozano-Pérez, 2017, Robotics: Science and Systems.

Learning to Rank for Synthesizing Planning Heuristics

Leslie Pack Kaelbling, Tomás Lozano-Pérez, Caelan Reed Garrett, 2016, IJCAI.

Associative Reinforcement Learning: Functions in k-DNF

L. Kaelbling, 1994, Machine Learning.

A Formal Framework for Learning in Embedded Systems

Leslie Pack Kaelbling, L. Kaelbling, 1989, ML.

Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness

Vikash K. Mansinghka, L. Kaelbling, Aidan Curtis, 2024, Robotics: Science and Systems XX.