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Compact Belief State Representation for Task Planning
[article]
2020
arXiv
pre-print
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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
2010
The international journal of robotics research
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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]
2018
arXiv
pre-print
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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
2014
International Journal of Computer and Electrical Engineering
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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
2005
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05)
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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]
2011
Universitext
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A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2013; you can also visit the original URL.
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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]
2013
Markov Decision Processes in Artificial Intelligence
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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
2010
The international journal of robotics research
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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
2004
Twenty-first international conference on Machine learning - ICML '04
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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]
2005
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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
2018
2018 IEEE International Conference on Robotics and Automation (ICRA)
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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
2018
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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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]
2019
arXiv
pre-print
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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]
2013
arXiv
pre-print
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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
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