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Learning Space Partitions for Path Planning
[article]
2022
arXiv
pre-print
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Recently, LaMCTS empirically learns to partition the search space in a reward-sensitive manner for black-box optimization. ...
We also propose a new path planning method LaP3 which improves the function value estimation within each sub-region, and uses a latent representation of the search space. ...
Acknowledgments and Disclosure of Funding We thank the members of the Berkeley NLP group as well as our four anonymous reviewers for their helpful feedback. ...
arXiv:2106.10544v4
fatcat:w5pqekogfzc6vniwnnrsqjq2yu
Partition Learning for Multiagent Planning
2012
Journal of Robotics
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The first step is target track estimation and the second step is path planning by optimizing directly over target track estimation. This standard approach works well in many scenarios. ...
However, an improved approach is needed for the scenario when general, nonparametric estimation is required, and the number of targets is unknown. ...
However, the average partition size decreases substantially for partition learning classification path planning. ...
doi:10.1155/2012/590479
fatcat:pshmruznenffvh2ozvvkwhftse
A Machine Learning Approach for Feature-Sensitive Motion Planning
[chapter]
2005
Springer Tracts in Advanced Robotics
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We use a machine learning approach to characterize and partition C-space into (possibly overlapping) regions that are well suited to one of the planners in our library of roadmap-based motion planning ...
In this paper, we propose an automated framework for feature-sensitive motion planning. ...
A prototype implementation of our machine learning classification and partitioning approach was shown to achieve results su- In the future, we plan to extend our framework to handle more C-space classes ...
doi:10.1007/10991541_25
fatcat:levnv4dy35cjtdqnfcqo2yfcr4
The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces
1995
Machine Learning
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Parti-game is a new algorithm for learning feasible trajectories to goal regions in high dimensional continuous state-spaces. ...
It develops a variable resolution partitioning in conjunction with the planning and learning of Algorithm 3. ...
In practice, parti-game's solution was shorter than the optimal path in the discretized maze. ...
doi:10.1007/bf00993591
fatcat:auxbn7yalvcmtpglg52j3bkwnq
Asymptotically Optimal Motion Planning for Learned Tasks Using Time-Dependent Cost Maps
2015
IEEE Transactions on Automation Science and Engineering
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Index Terms Motion and path planning; assistive robots Contact ...
We next present an efficient algorithm for planning robot motions to perform the task based on the learned features while avoiding obstacles. ...
Acknowledgments Alterovitz has co-authored a book on Motion Planning in Medicine, was awarded a patent for a medical device, has received multiple best paper finalist awards at IEEE robotics conferences ...
doi:10.1109/tase.2014.2342718
pmid:26279642
pmcid:PMC4535732
fatcat:a3xaxcvvpbgxzbgbstm36uqp5e
Towards Learning Path Planning for Solving Complex Robot Tasks
[chapter]
2001
Lecture Notes in Computer Science
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For solving complex robot tasks it is necessary to incorporate path planning methods that are able to operate within di erent high-dimensional con guration spaces containing an unknown number of obstacles ...
For an examplary planning task we show that Adaptive AA* learns movement v ectors which a l l o w larger movements than the initial ones into well-de ned directions of the con guration space. ...
Donath, Nils Goerke, and Stefan Ho richter for useful comments. ...
doi:10.1007/3-540-44668-0_130
fatcat:rxpk56edxvdu5lnmbhhtmqidf4
Learning sensor-based navigation of a real mobile robot in unknown worlds
1999
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
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The parti-game multiresolution learning approach [22] is applied for simultaneous and cooperative construction of a world model, and learning to navigate through an obstacle-free path from a starting position ...
Index Terms-Learning systems, mobile robot navigation. ...
Two of the most important tasks for autonomous navigation of a mobile robot are path planning, and building a world model. ...
doi:10.1109/3477.752791
pmid:18252290
fatcat:4fzw6aphzrcufjeisjjjds223u
Multi-robot path planning for budgeted active perception with self-organising maps
2016
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
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We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. ...
We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. ...
This initialisation is reasonable since the paths quickly spread over the input space during the first learning epochs.
B. Viewpoint Rewards Each node has an associated reward for being visited. ...
doi:10.1109/iros.2016.7759489
dblp:conf/iros/BestFF16
fatcat:mpfhbq6jhrfx7bjt6565vm6q74
Hierarchical Learning in Stochastic Domains: Preliminary Results
[chapter]
1993
Machine Learning Proceedings 1993
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This paper presents the HDG learning algorithm, which uses a hierarchical decomposition of the state space to make learning to achieve goals more efficient with a small penalty in path quality. ...
Special care must be taken when performing hierarchical planning and learning in stochastic domains, because macro-operators cannot be executed ballistically. ...
LANDMARK NETWORKS When we plan a route for driving a long distance, we don't even attempt to find the optimal path; rather, we take advantage of an existing path hierarchy. ...
doi:10.1016/b978-1-55860-307-3.50028-9
dblp:conf/icml/Kaelbling93
fatcat:jhxt6fv4tzfrbkwsbkwvhcvmqi
Taking Learning Out of Real-Time Heuristic Search for Video-Game Pathfinding
[chapter]
2010
Lecture Notes in Computer Science
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The new approach has a fast move time and eliminates learning and "scrubbing". ...
However, a common issue is that the pre-computation time can be large, and there is no guarantee that the precomputed data adequately covers the search space. ...
The edge costs are 1 for cardinal moves and 1.4 for diagonal moves. An agent plans its next action by considering states in a local search space surrounding its current position. ...
doi:10.1007/978-3-642-17432-2_41
fatcat:sdydtuk7yngedmhefa552lxmva
Automatic Curriculum Learning For Deep RL: A Short Survey
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL). ...
They can optimize domain randomization for Sim2Real transfer, organize task presentations in multi-task robotic settings, order sequences of opponents in multi-agent scenarios, etc. ...
Initial discussions started at the Dagstuhl seminar 18071 on Planning and Operations Research. ...
doi:10.24963/ijcai.2020/663
dblp:conf/ijcai/PommereningRHCR20
fatcat:uazsfxe2lne4bljtm42moao6jq
A Non-uniform Sampling Approach for Fast and Efficient Path Planning
[article]
2021
arXiv
pre-print
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In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. ...
This sampling framework is incorporated into the RRT* path planner. ...
Section II formulates the path planning problem for AUVs. Section III presents the smart sampling procedure and the path planning algorithm. ...
arXiv:2108.01291v1
fatcat:vfwow7eeffblzoplhqimng7q3u
Learning Abstract and Transferable Representations for Planning
[article]
2022
arXiv
pre-print
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We restrict our focus to learning representations for long-term planning, a class of problems that state-of-the-art learning methods are unable to solve. ...
We propose a framework for autonomously learning state abstractions of an agent's environment, given a set of skills. ...
We intend to learn an abstract representation suitable for planning. ...
arXiv:2205.02092v1
fatcat:cbu5i3j7gbdbpiidircx5zdsu4
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search
[article]
2022
arXiv
pre-print
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2020 pre-print arXiv:2007.00708v1 fatcat:naxmckuw2bgwxldp4xdop7vqva
Unlike previous approaches, LA-MCTS learns the partition of the search space using a few samples and their function values in an online fashion. ...
., BO, TuRBO) as its local models, achieving strong performance in general black-box optimization and reinforcement learning benchmarks, in particular for high-dimensional problems. ...
Following the path of learning, one under-explored direction is to learn the space partition. ...
arXiv:2007.00708v2
fatcat:l7wgsqtzxff6ngd4jhomov7tve
PPVF: A Novel Framework for Supporting Path Planning Over Carpooling
2019
IEEE Access
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In this paper, we propose an efficient framework, named PPVF(short for path prediction and verification-based framework) for path planning over the road network. ...
Furthermore, we propose a searching and verification-based algorithm for further improving the path planning quality. ...
For the path partition, we partition the elements in KP into a few sub-paths, use a group of MBRs with relatively small area to bound these paths. ...
doi:10.1109/access.2019.2891570
fatcat:j7ydj34bn5birooihutp3qhjly
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