Filters








20,116 Hits in 3.6 sec

Compact Belief State Representation for Task Planning [article]

Evgenii Safronov, Michele Colledanchise, Lorenzo Natale
2020 arXiv   pre-print
We show that AOBS representation is not only much more compact than a full belief state but it also scales better than BDD for most of the cases.  ...  The performance of a task planner relies on the belief state representation.  ...  We developed a novel probabilistic belief state representation based on an And Or direct acyclic graph named AOBS.  ... 
arXiv:2008.10386v1 fatcat:ilsln6asuzaevbrh6petg4xkwe

Motion planning under uncertainty for robotic tasks with long time horizons

Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Lee
2010 The international journal of robotics research  
MiGS samples a set of points, called milestones, from a robot's state space, uses them to construct a compact, sampled representation of the state space, and then uses this representation of the state  ...  However, robot motion planning tasks with long time horizons remain a severe obstacle for even the fastest point-based POMDP solvers today.  ...  Acknowledgements We thank Sylvie Ong and Shao Wei Png for reading the first draft of this paper and helping with scripting a POMDP model.  ... 
doi:10.1177/0278364910386986 fatcat:wm2pkgpgcbewvc6gyjiycxo4tm

Integrating Human-Provided Information Into Belief State Representation Using Dynamic Factorization [article]

Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
2018 arXiv   pre-print
In this paper, we provide an efficient belief state representation that dynamically selects an appropriate factoring, combining aspects of the belief when they are correlated through information and separating  ...  for complex partially observed tasks.  ...  Factor graphs provide a compact representation for the factorization of a function.  ... 
arXiv:1803.00119v4 fatcat:g2raylikgzd6tjnxdemy4zoloa

Robot Task Planning on Multiple Environments

Paulo Pinheiro, Jacques Wainer, Eleri Cardozo
2014 International Journal of Computer and Electrical Engineering  
Lighter POMDP plans are generated only for these compact maps and not for the whole environment, decreasing size of the set of possible states.  ...  This paper presents a hierarchical planning approach to solving the global localization problem for mobile robots using a compaction map method.  ...  The Table 1 shows the comparative between the original map and the new compact representation for number of states on the grid and number of plans precomputed.  ... 
doi:10.17706/ijcee.2014.v6.863 fatcat:r6ho5nfun5capebqg6kefulnwe

Planning with POMDPs using a compact, logic-based representation

Chenggang Wang, J. Schmolze
2005 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)  
The key contribution is our compact representation of belief states and of the operations used to update them.  ...  Representations built on logic allow for problems to be specified in a compact and transparent manner.  ...  Representing belief states The final representation is for π, the belief state. Here we use the sum term.  ... 
doi:10.1109/ictai.2005.96 dblp:conf/ictai/WangS05 fatcat:o2jpm75u4jblvkbiaa2kyk54ee

Partially Observable Markov Decision Processes [chapter]

Nicole Bäuerle, Ulrich Rieder
2011 Universitext  
The key contribution is our compact representation of belief states and of the operations used to update them.  ...  Representations built on logic allow for problems to be specified in a compact and transparent manner.  ...  Representing belief states The final representation is for π, the belief state. Here we use the sum term.  ... 
doi:10.1007/978-3-642-18324-9_5 fatcat:uy4klthvpjcbnpsey4gwzn57se

Partially Observable Markov Decision Processes [chapter]

Alain Dutech, Bruno Scherrer
2013 Markov Decision Processes in Artificial Intelligence  
The key contribution is our compact representation of belief states and of the operations used to update them.  ...  Representations built on logic allow for problems to be specified in a compact and transparent manner.  ...  Representing belief states The final representation is for π, the belief state. Here we use the sum term.  ... 
doi:10.1002/9781118557426.ch7 fatcat:uhipce426zexbbfxp7nwvip6sq

Planning under Uncertainty for Robotic Tasks with Mixed Observability

Sylvie C. W. Ong, Shao Wei Png, David Hsu, Wee Sun Lee
2010 The international journal of robotics research  
We use a factored model to represent separately the fully and partially observable components of a robot's state and derive a compact lower-dimensional representation of its belief space.  ...  Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments.  ...  The authors would like to thank Yanzhu Du for helping with the software implementation. We also thank Tomás Lozano-Pérez and Leslie Kaelbling from MIT for many insightful discussions.  ... 
doi:10.1177/0278364910369861 fatcat:cgnvlxyypndhxjaavxvkndkw6e

Learning low dimensional predictive representations

Matthew Rosencrantz, Geoff Gordon, Sebastian Thrun
2004 Twenty-first international conference on Machine learning - ICML '04  
tracking task for low dimensional representations and long prediction horizons.  ...  Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dynamical system (Littman  ...  One of the motivations for finding compact representations is the hope that they will result in easier planning problems.  ... 
doi:10.1145/1015330.1015441 dblp:conf/icml/RosencrantzGT04 fatcat:teh37dqgzbg4vbik2owmqszq2u

Towards Comprehensive Computational Models for Plan-Based Control of Autonomous Robots [chapter]

Michael Beetz
2005 Lecture Notes in Computer Science  
We will argue that the development of comprehensive and integrated computational models of plan-based control requires us to consider different aspects of plan-based control -plan representation, reasoning  ...  We identify computational principles that enable autonomous robots to accomplish complex, diverse, and dynamically changing tasks in challenging environments.  ...  A representationally adequate plan representation for robotic agents must also support the control and proper use of the robot's different mechanisms for perception, deliberation, action, and communication  ... 
doi:10.1007/978-3-540-32254-2_29 fatcat:wdlop6bwdbf7hm5dzmbpzjft6i

Act, Perceive, and Plan in Belief Space for Robot Localization [article]

Michele Colledanchise, Damiano Malafronte, Lorenzo Natale
2020 arXiv   pre-print
an object or asking someone for a direction.  ...  In our approach, a task planner computes a sequence of action and perception tasks to actively obtain relevant information from the robot's perception system.  ...  By planning in belief space, the search algorithm operates in a compact representation of the knowledge about the world, which results in better planning performance.  ... 
arXiv:2002.08124v3 fatcat:ttppnhlilbahrd6rubrueqbdg4

Reliably Arranging Objects in Uncertain Domains

Ariel S. Anders, Leslie P. Kaelbling, Tomas Lozano-Perez
2018 2018 IEEE International Conference on Robotics and Automation (ICRA)  
Conformant planning is a belief-state planning problem.  ...  To do forward belief-state planning, we created a deterministic belief-state transition model from supervised learning based on off-line physics simulations.  ...  In order to fight this curse of dimensionality we use a factored representation of belief states.  ... 
doi:10.1109/icra.2018.8462892 dblp:conf/icra/AndersKL18 fatcat:biqxzakn6zbpjevevc6vidiguq

Approaches for Action Sequence Representation in Robotics: A Review

Hirenkumar Nakawala, Paulo J. S. Goncalves, Paolo Fiorini, Giancarlo Ferringo, Elena De Momi
2018 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
In this manuscript, we present a review of literature dealing with representation of action and action sequences for robot task planning and execution.  ...  Robust representation of actions and its sequences for complex robotic tasks would transform robot's understanding to execute robotic tasks efficiently.  ...  for task planning and execution.  ... 
doi:10.1109/iros.2018.8594256 dblp:conf/iros/NakawalaGFFM18 fatcat:aamljxaqyvbnvjpoqulgmefc7a

Improving Performance of Multiagent Cooperation Using Epistemic Planning [article]

Abeer Alshehri School of Computing and Information Systems, University of Melbourne, Victoria, Australia Department of Computer Science and Information Systems, King Khalid University, Abha, Saudi Arabia)
2019 arXiv   pre-print
In this paper, we draw inspiration from studies of epistemic planning to develop a communication model for agents that allows them to cooperate and make communication decisions effectively within a planning  ...  task.  ...  Ronal Singh for valuable support during the experiment process.  ... 
arXiv:1910.02607v1 fatcat:cqqupdm6j5cipmxqlypva4nhwq

Tractable Inference for Complex Stochastic Processes [article]

Xavier Boyen, Daphne Koller
2013 arXiv   pre-print
Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable.  ...  In the case of a stochastic system, these tasks typically involve the use of a belief state- a probability distribution over the state of the process at a given point in time.  ...  Acknowledgements We gratefully acknowledge Eric Bauer, Lise Getoor, and Uri Lerner for work on the software used in the experi-ments, Raya Fratkina for help with the network files, and Tim Huang for providing  ... 
arXiv:1301.7362v1 fatcat:r7coczhoavg7hhdlgz65gwag6i
« Previous Showing results 1 — 15 out of 20,116 results