Planning and execution through variable resolution planning

Martínez, Moisés; Fernández, Fernando; Borrajo, Daniel

doi:10.1016/j.robot.2016.04.009

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…For all the domains and the configurations proposed, CB-PELEA outperforms the response time of FD-Replan and OAKPlan 44 . Taking into account the largest prob-May 19, 2019 10:1 WSPC/ws-ijitdm output 34 Martínez, García and Fernández lems for each domain, the response time of CB-PELEA is 40% faster than the response time of OAKPlan, and 53% faster than the response time of FD-Replan. This is particularly interesting in realistic environments where there are more and more unexpected situations, and where timely responses in such situations is really essential.…”

Section: Discussionmentioning

confidence: 99%

“…This may be seen as a dramatic effect which precludes its application to real world problems. However, replanners systems based solely on deterministic planning have been successfully used in real-world domains known for unexpected outcomes 40,29,12,34 . In fact, deterministic planning is the most preferable option used in execution-monitoring-planning loops.…”

Section: Introductionmentioning

confidence: 99%

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On-Line Case-Based Policy Learning for Automated Planning in Probabilistic Environments

Martínez

Garcı́a

Fernández

2018

Int. J. Info. Tech. Dec. Mak.

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Many robotic control architectures perform a continuous cycle of sensing, reasoning and acting, where that reasoning can be carried out in a reactive or deliberative form. Reactive methods are fast and provide the robot with high interaction and response capabilities. Deliberative reasoning is particularly suitable in robotic systems because it employs some form of forward projection (reasoning in depth about goals, pre-conditions, resources and timing constraints) and provides the robot reasonable responses in situations unforeseen by the designer. However, this reasoning, typically conducted using Artificial Intelligence techniques like Automated Planning (AP), is not effective for controlling autonomous agents which operate in complex and dynamic environments. Deliberative planning, although feasible in stable situations, takes too long in unexpected or changing situations which require re-planning. Therefore, planning cannot be done on-line in many complex robotic problems, where quick responses are frequently required. In this paper, we propose an alternative approach based on case-based policy learning which integrates deliberative reasoning through AP and reactive response time through reactive planning policies. The method is based on learning planning knowledge from actual experiences to obtain a case-based policy. The contribution of this paper is two fold. First, it is shown that the learned case-based policy produces reasonable and timely responses in complex environments. Second, it is also shown how one case-based policy that solves a particular problem can be reused to solve a similar but more complex problem in a transfer learning scope.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On-Line Case-Based Policy Learning for Automated Planning in Probabilistic Environments

Martínez

Garcı́a

Fernández

2018

Int. J. Info. Tech. Dec. Mak.

Self Cite

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“…Our approach is also similar to "variable resolution" [Martínez et al, 2016] in that exact search is limited to a certain depth. After that depth, the two approaches differ.…”

Section: Related Workmentioning

confidence: 99%

“…After that depth, the two approaches differ. In particular, a pessimistic scenario cannot be obtained by "removing some information from the planning task," as is done in Martínez et al [2016].…”

Section: Related Workmentioning

confidence: 99%

Real-time tree search with pessimistic scenarios

Osogami,

Takahashi

2019

Preprint

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Autonomous agents need to make decisions in a sequential manner, under partially observable environment, and in consideration of how other agents behave. In critical situations, such decisions need to be made in real time for example to avoid collisions and recover to safe conditions. We propose a technique of tree search where a deterministic and pessimistic scenario is used after a specified depth. Because there is no branching with the deterministic scenario, the proposed technique allows us to take into account the events that can occur far ahead in the future. The effectiveness of the proposed technique is demonstrated in Pommerman, a multi-agent environment used in a NeurIPS 2018 competition, where the agents that implement the proposed technique have won the first and third places.

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“…In this case, replanning is required to take this additional state information into consideration. To resolve these issues, scholars [28,29,30,31,32] have suggested planning short term actions in more detail than those later in the planning horizon.…”

Section: Related Workmentioning

confidence: 99%

Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

Harman

Chintamani

Simoens

2019

Sensors

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By coupling a robot to a smart environment, the robot can sense state beyond the perception range of its onboard sensors and gain greater actuation capabilities. Nevertheless, incorporating the states and actions of Internet of Things (IoT) devices into the robot’s onboard planner increases the computational load, and thus can delay the execution of a task. Moreover, tasks may be frequently replanned due to the unanticipated actions of humans. Our framework aims to mitigate these inadequacies. In this paper, we propose a continual planning framework, which incorporates the sensing and actuation capabilities of IoT devices into a robot’s state estimation, task planing and task execution. The robot’s onboard task planner queries a cloud-based framework for actuators, capable of the actions the robot cannot execute. Once generated, the plan is sent to the cloud back-end, which will inform the robot if any IoT device reports a state change affecting its plan. Moreover, a Hierarchical Continual Planning in the Now approach was developed in which tasks are split-up into subtasks. To delay the planning of actions that will not be promptly executed, and thus to reduce the frequency of replanning, the first subtask is planned and executed before the subsequent subtask is. Only information relevant to the current (sub)task is provided to the task planner. We apply our framework to a smart home and office scenario in which the robot is tasked with carrying out a human’s requests. A prototype implementation in a smart home, and simulator-based evaluation results, are presented to demonstrate the effectiveness of our framework.

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Planning and execution through variable resolution planning

Cited by 6 publications

References 22 publications

On-Line Case-Based Policy Learning for Automated Planning in Probabilistic Environments

On-Line Case-Based Policy Learning for Automated Planning in Probabilistic Environments

Real-time tree search with pessimistic scenarios

Robot Assistance in Dynamic Smart Environments—A Hierarchical Continual Planning in the Now Framework

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