Implicit belief-space pre-images for hierarchical planning and execution

Kaelbling, Leslie Pack; Lozano-Pérez, Tomás

doi:10.1109/icra.2016.7487758

Cited by 9 publications

(8 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many approaches for representing and updating a belief such as joint, unscented Kalman filtering [23], [7], factoring the belief into independent distributions per object [15], [25], and maintaining a particle filter, which represents the belief as a set of weighted samples [17], [18], [19], [21]. Many approaches use a different belief representation when planning versus when filtering.…”

Section: Related Workmentioning

confidence: 99%

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

Garrett¹,

Paxton²,

Lozano-Pérez³

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

Garrett¹,

Paxton²,

Lozano-Pérez³

et al. 2020

2020 IEEE International Conference on Robotics and Automation (ICRA)

Self Cite

View full text Add to dashboard Cite

“…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%

“…The authors use a hierarchical planning language in which the actions must be labelled with the level of abstraction, and the complete world state is always reasoned on. This has been expanded with belief states, that is, Belief Hierarchical Planning in the Now (BHPN), and planning using continuous state information (e.g., position) [30].…”

Section: Related Workmentioning

confidence: 99%

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

Harman

Chintamani

Simoens

2019

Sensors

View full text Add to dashboard Cite

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.

show abstract

“…A body of relevant previous work incorporates heuristic search and classical AI techniques in algorithms for solving MDPs [21,22,23]. Several works from the robotics planning community solve related problems using local information to inform global planning and trajectory optimization [24,25] and explore various aspects of combined (discrete) task and (continuous) motion planning [3,2] such as planner-agnostic abstractions [26], stochastic shortest path formulations [27,28], and hierarchical planning and execution [29,30]. Our optimization-based formulation is similar to that for Logic-Geometric Programming [31].…”

Section: Related Work Overviewmentioning

confidence: 99%

Hybrid Planning for Dynamic Multimodal Stochastic Shortest Paths

Choudhury,

Kochenderfer

2019

Preprint

View full text Add to dashboard Cite

Sequential decision problems in applications such as manipulation in warehouses, multi-step meal preparation, and routing in autonomous vehicle networks often involve reasoning about uncertainty, planning over discrete modes as well as continuous states, and reacting to dynamic updates. To formalize such problems generally, we introduce a class of Markov Decision Processes (MDPs) called Dynamic Multimodal Stochastic Shortest Paths (DMSSPs). Much of the work in these domains solves deterministic variants, which can yield poor results when the uncertainty has downstream effects. We develop a Hybrid Stochastic Planning (HSP) algorithm, which uses domain-agnostic abstractions to efficiently unify heuristic search for planning over discrete modes, approximate dynamic programming for stochastic planning over continuous states, and hierarchical interleaved planning and execution. In the domain of autonomous multimodal routing, HSP obtains significantly higher quality solutions than a state-of-the-art Upper Confidence Trees algorithm and a two-level Receding Horizon Control algorithm.Preprint. Under review.

show abstract

Implicit belief-space pre-images for hierarchical planning and execution

Cited by 9 publications

References 28 publications

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

Online Replanning in Belief Space for Partially Observable Task and Motion Problems

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

Hybrid Planning for Dynamic Multimodal Stochastic Shortest Paths

Contact Info

Product

Resources

About