Fetching the paper…
Reading the bibliography…
Planning under uncertainty is critical to robotics.
Observation of a Markov process through a noisy channel
A. W. Drake · 1962
Earlier work this paper cites.
The optimal control of partially observable Markov processes
E. J. Sondik · 1971
Earlier work this paper cites.
The optimal control of partially observable markov processes over a finite horizon
R. D. Smallwood and E. J. Sondik · 1973
Earlier work this paper cites.
The optimal control of partially observable markov processes over the infinite horizon: Discounted costs
E. J. Sondik · 1978
Earlier work this paper cites.
An algorithm for planning collision-free paths among polyhedral obstacles
T. Lozano-Pérez and M. A. Wesley · 1979
Earlier work this paper cites.
State of the art—a survey of partially observable markov decision processes: theory, models, and algorithms
G. E. Monahan · 1982
Earlier work this paper cites.
On moving and orienting objects
B. K. Natarajan · 1986
Earlier work this paper cites.
New lower bound techniques for robot motion planning problems
J. Canny and J. Reif · 1987
Earlier work this paper cites.
The complexity of markov decision processes
C. H. Papadimitriou and J. N. Tsitsiklis · 1987
Earlier work this paper cites.
Algorithms for partially observable Markov decision processes
H. Cheng · 1988
Earlier work this paper cites.
A monte-carlo algorithm for path planning with many degrees of freedom
J. Barraquand and J.-C. Latombe · 1990
Earlier work this paper cites.
The ”Ariadne’s clew” algorithm: Global planning with local methods
P. Bessiere, J.-M. Ahuactzin, E.-G. Talbi, and E. Mazer · 1993
Earlier work this paper cites.
Obbtree: A hierarchical structure for rapid interference detection
S. Gottschalk, M. C. Lin, and D. Manocha · 1996
Earlier work this paper cites.
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
L. E. Kavraki, P. Svestka, J.-C. Latombe, and M. H. Overmars · 1996
Earlier work this paper cites.
Path planning in expansive configuration spaces
D. Hsu, J.-C. Latombe, and R. Motwani · 1997
Earlier work this paper cites.
Solving large POMDPs using real time dynamic programming
B. Bonet · 1998
Earlier work this paper cites.
Solving POMDPs by searching in policy space
E. A. Hansen · 1998
Earlier work this paper cites.
Planning and acting in partially observable stochastic domains
L. P. Kaelbling, M. L. Littman, and A. R. Cassandra · 1998
Earlier work this paper cites.
Spudd: Stochastic planning using decision diagrams
J. Hoey, R. St-Aubin, A. Hu, and C. Boutilier · 1999
Earlier work this paper cites.
Fast proximity queries with swept sphere volumes
E. Larsen, S. Gottschalk, M. C. Lin, and D. Manocha · 1999
Earlier work this paper cites.
Monte carlo pomdps
S. Thrun · 1999
Earlier work this paper cites.
Value-function approximations for partially observable markov decision processes
M. Hauskrecht · 2000
Earlier work this paper cites.
Complexity of finite-horizon markov decision process problems
M. Mundhenk, J. Goldsmith, C. Lusena, and E. Allender · 2000
Earlier work this paper cites.
PEGASUS: A Policy Search Method for Large MDPs and POMDPs
A. Y. Ng and M. Jordan · 2000
Earlier work this paper cites.
Speeding up the convergence of value iteration in partially observable markov decision processes
N. L. Zhang and W. Zhang · 2001
Earlier work this paper cites.
Finite-time analysis of the multiarmed bandit problem
P. Auer, N. Cesa-Bianchi, and P. Fischer · 2002
Earlier work this paper cites.
Point-based value iteration: An anytime algorithm for POMDPs
J. Pineau, G. Gordon, S. Thrun, et al · 2003
Earlier work this paper cites.
Approximate planning in pomdps with macro-actions
G. Theocharous and L. Kaelbling · 2003
Cited alongside, same era.
Heuristic search value iteration for POMDPs
T. Smith and R. Simmons · 2004
Cited alongside, same era.
Solving POMDPs with continuous or large discrete observation spaces
J. Hoey and P. Poupart · 2005
Cited alongside, same era.
Exploiting structure to efficiently solve large scale partially observable Markov decision processes
P. Poupart · 2005
Cited alongside, same era.
Point-based POMDP algorithms: Improved analysis and implementation
T. Smith and R. Simmons · 2005
Cited alongside, same era.
Perseus: Randomized point-based value iteration for POMDPs
M. T. Spaan and N. Vlassis · 2005
Cited alongside, same era.
An online pomdp solver for uncertainty planning in dynamic environment
H. Kurniawati and V. Yadav · 2013
Later among the works it cites.
A survey of point-based POMDP solvers
G. Shani, J. Pineau, and R. Kaplow · 2013
Later among the works it cites.
DESPOT: Online POMDP Planning with Regularization
A. Somani, N. Ye, D. Hsu, and W. S. Lee · 2013
Later among the works it cites.
Firm: Sampling-based feedback motion-planning under motion uncertainty and imperfect measurements
A.-A. Agha-Mohammadi, S. Chakravorty, and N. M. Amato · 2014
Later among the works it cites.
Integrated perception and planning in the continuous space: A POMDP approach
H. Bai, D. Hsu, and W. S. Lee · 2014
Later among the works it cites.
Bayesian Reinforcement Learning: A Survey
M. Ghavamzadeh, S. Mannor, J. Pineau, and A. Tamar · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Bandit based monte-carlo planning
L. Kocsis and C. Szepesvári · 2006
Cited alongside, same era.
Point-based value iteration for continuous POMDPs
J. M. Porta, N. Vlassis, M. T. Spaan, and P. Poupart · 2006
Cited alongside, same era.
What makes some POMDP problems easy to approximate?
D. Hsu, W. Lee, and N. Rong · 2007
Cited alongside, same era.
Point-based policy iteration
S. Ji, R. Parr, H. Li, X. Liao, and L. Carin · 2007
Cited alongside, same era.
Continuous-State POMDPs with Hybrid Dynamics
E. Brunskill, L. P. Kaelbling, T. Lozano-Perez, and N. Roy · 2008
Cited alongside, same era.
SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces
H. Kurniawati, D. Hsu, and W. Lee · 2008
Cited alongside, same era.
Deep recurrent Q-learning for partially observable MDPs
M. Hausknecht and P. Stone · 2015
Later among the works it cites.
The Importance of a Suitable Distance Function in Belief-Space Planning
Z. Littlefield, D. Klimenko, H. Kurniawati, and K. E. Bekris · 2015
Later among the works it cites.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
Later among the works it cites.
An Online and Approximate Solver for POMDPs with Continuous Action Space
K. Seiler, H. Kurniawati, and S. Singh · 2015
Later among the works it cites.
Non-Linearity Measure for POMDP-based Motion Planning
M. Hoerger, H. Kurniawati, and A. Elfes · 2016
Later among the works it cites.
Learning to navigate in complex environments
P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. J. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu, D. Kumaran, and R. Hadsell · 2016
Later among the works it cites.
Bayesian optimisation for solving continuous state-action-observation pomdps
P. Morere, R. Marchant, and F. Ramos · 2016
Later among the works it cites.
Reinforcement learning via recurrent convolutional neural networks
T. Shankar, S. K. Dwivedy, and P. Guha · 2016
Later among the works it cites.
QMDP-net: Deep learning for planning under partial observability
P. Karkus, D. Hsu, and W. S. Lee · 2017
Later among the works it cites.
Value prediction network
J. Oh, S. Singh, and H. Lee · 2017
Later among the works it cites.
Deep variational reinforcement learning for POMDPs
M. Igl, L. Zintgraf, T. A. Le, F. Wood, and S. Whiteson · 2018
Later among the works it cites.
Online algorithms for POMDPs with continuous state, action, and observation spaces
Z. N. Sunberg and M. J. Kochenderfer · 2018
Later among the works it cites.
An On-line Planner for POMDPs with Large Discrete Action Space: A Quantile-Based Approach
E. Wang, H. Kurniawati, and D. Kroese · 2018
Later among the works it cites.
Multilevel monte-carlo for solving POMDPs online
M. Hoerger, H. Kurniawati, and A. Elfes · 2019
Later among the works it cites.
POMDP-based Candy Server: Lessons Learned from a Seven Day Demo
M. Hoerger, J. Song, H. Kurniawati, and A. Elfes · 2019
Later among the works it cites.
POMHDP: Search-based belief space planning using multiple heuristics
S.-K. Kim, O. Salzman, and M. Likhachev · 2019
Later among the works it cites.
Inventory control with partially observable states
E. Wang, H. Kurniawati, and D. Kroese · 2019
Later among the works it cites.
Locally-connected interrelated network: A forward propagation primitive
N. Collins and H. Kurniawati · 2020
Later among the works it cites.
Belief-dependent macro-action discovery in POMDPs using the value of information
G. Flaspohler, N. A. Roy, and J. W. Fisher III · 2020
Later among the works it cites.
An on-line pomdp solver for continuous observation spaces
M. Hoerger and H. Kurniawati · 2021
Closest in time.
Bayesian optimized monte carlo planning
J. Mern, A. Yildiz, Z. Sunberg, T. Mukerji, and M. J. Kochenderfer · 2021
Closest in time.