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Integrated task and motion planning (TAMP) has proven to be a valuable approach to generalizable long-horizon robotic manipulation and navigation problems.
Anytime integrated task and motion policies for stochastic environments
Naman Shah and Siddharth Srivastava · 1904
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Online replanning in belief space for partially observable task and motion problems
Caelan Reed Garrett, Chris Paxton, Tomás Lozano-Pérez, Leslie Pack Kaelbling, and Dieter Fox · 1911
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The optimal control of partially observable markov process over the infinite horizon: Discounted costs
Edward Sondik · 1978
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Lao*: A heuristic search algorithm that finds solutions with loops
Eric A. Hansen and Shlomo Zilberstein · 2001
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Weak, strong, and strong cyclic planning via symbolic model checking
A. Cimatti, M. Pistore, M. Roveri, and P. Traverso · 2003
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Ppddl 1 . 0 : An extension to pddl for expressing planning domains with probabilistic effects
Håkan L. S. Younes and Michael L. Littman · 2004
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Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
Sebastian Thrun, Wolfram Burgard, and Dieter Fox · 2005
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Can autonomous vehicles identify, recover from, and adapt to distribution shifts?
Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, and Yarin Gal · 2006
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The fast downward planning system
Malte Helmert · 2006
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Bayesian robust optimization for imitation learning
Daniel S. Brown, Scott Niekum, and Marek Petrik · 2007
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Ff-replan: A baseline for probabilistic planning
Sung Wook Yoon, Alan Fern, and Robert Givan · 2007
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Monte-Carlo planning in large POMDPs
David Silver and Joel Veness · 2010
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Integrated task and motion planning
Caelan Reed Garrett, Rohan Chitnis, Rachel M. Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2010
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On bayesian upper confidence bounds for bandit problems
Emilie Kaufmann, Olivier Cappe, and Aurelien Garivier · 2012
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Integrated task and motion planning in belief space
Leslie Kaelbling and Tomas Lozano-Perez · 2013
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Modular task and motion planning in belief space
Dylan Hadfield-Menell, Edward Groshev, Rohan Chitnis, and Pieter Abbeel · 2015
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DESPOT: online POMDP planning with regularization
Nan Ye, Adhiraj Somani, David Hsu, and Wee Sun Lee · 2016
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POMCPOW: an online algorithm for pomdps with continuous state, action, and observation spaces
Zachary Sunberg and Mykel J. Kochenderfer · 2017
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Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick · 2017
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Stripstream: Integrating symbolic planners and blackbox samplers
Caelan Reed Garrett, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2018
Isaac gym: High performance gpu-based physics simulation for robot learning, 2021
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, and Gavriel State · 2021
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Robust policy learning over multiple uncertainty sets, 2022
Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, and Amy Zhang · 2022
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Symbolic Search for Optimal Planning with Expressive Extensions
David Speck · 2022
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Task-directed exploration in continuous pomdps for robotic manipulation of articulated objects, 2022
Aidan Curtis, Leslie Kaelbling, and Siddarth Jain · 2022
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Robust-rrt: Probabilistically-complete motion planning for uncertain nonlinear systems, 2022
Albert Wu, Thomas Lew, Kiril Solovey, Edward Schmerling, and Marco Pavone · 2022
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PyBullet Planning
Caelan Reed Garrett · 2018
Cited alongside, same era.
Gen: a general-purpose probabilistic programming system with programmable inference
Marco F Cusumano-Towner, Feras A Saad, Alexander K Lew, and Vikash K Mansinghka · 2019
Cited alongside, same era.
Controlling contact-rich manipulation under partial observability
Florian Wirnshofer, Philipp S. Schmitt, Georg von Wichert, and Wolfram Burgard · 2020
Cited alongside, same era.
An introduction to sequential Monte Carlo , volume 4
Nicolas Chopin, Omiros Papaspiliopoulos, et al · 2020
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Automating involutive mcmc using probabilistic and differentiable programming
Marco Cusumano-Towner, Alexander K Lew, and Vikash K Mansinghka · 2020
Cited alongside, same era.
Symbolic top-k planning
David Speck, Robert Mattmüller, and Bernhard Nebel · 2020
Cited alongside, same era.
Uncertainty-aware model-based reinforcement learning with application to autonomous driving
Jingda Wu, Zhiyu Huang, and Chen Lv · 2021
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Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, and João G.M. Araújo · 2022
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Global planning for contact-rich manipulation via local smoothing of quasi-dynamic contact models, 2023
Tao Pang, H. J. Terry Suh, Lujie Yang, and Russ Tedrake · 2023
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Learning efficient abstract planning models that choose what to predict, 2023
Nishanth Kumar, Willie McClinton, Rohan Chitnis, Tom Silver, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2023
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Sequence-based plan feasibility prediction for efficient task and motion planning, 2023
Zhutian Yang, Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Kaelbling, and Dieter Fox · 2023
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From word models to world models: Translating from natural language to the probabilistic language of thought, 2023
Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, and Joshua B. Tenenbaum · 2023
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Bayes3d: fast learning and inference in structured generative models of 3d objects and scenes
Nishad Gothoskar, Matin Ghavami, Eric Li, Aidan Curtis, Michael Noseworthy, Karen Chung, Brian Patton, William T Freeman, Joshua B Tenenbaum, Mirko Klukas, et al · 2023
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3d neural embedding likelihood: Probabilistic inverse graphics for robust 6d pose estimation
Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, and Vikash K Mansinghka · 2023
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Predicate invention for bilevel planning
Tom Silver, Rohan Chitnis, Nishanth Kumar, Willie McClinton, Tomás Lozano-Pérez, Leslie Kaelbling, and Joshua B. Tenenbaum · 2023
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Motions in microseconds via vectorized sampling-based planning
Wil Thomason, Zachary Kingston, and Lydia E. Kavraki · 2024
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Contingent task and motion planning under uncertainty for human–robot interactions
Aliakbar Akbari, Mohammed Diab, and Jan Rosell · 2076
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