Anytime Integrated Task and Motion Policies for Stochastic Environments

Shah, Naman; Vasudevan, Deepak Kala; Kumar, Kislay; Kamojjhala, Pranav; Srivastava, Siddharth

doi:10.1109/icra40945.2020.9197574

Cited by 8 publications

(10 citation statements)

References 20 publications

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“…Refining each possible outcome in the policy can take a substantial amount of time. However, ATAM algorithm (Shah et al 2020) reduces the problem of selecting scenarios for refinement to a knapsack problem and use a greedy approach to prioritize more likely outcomes for refinement. The empirical evaluation shows that this approach allows the robot to start executing action much earlier compared to when actions are selected randomly.…”

Section: Stochastic Task and Motion Planningmentioning

confidence: 99%

“…However, robot planning over a long horizon is challenging due to the continuous state and action spaces of the robot. Hierarchical approaches (Garrett, Lozano-Pérez, and Kaelbling 2020; Shah et al 2020) have shown that such abstractions can also be used for efficient robot planning. Unfortunately, these approaches require sound abstractions that are consistent with the motion planning of the robot.…”

Section: Introductionmentioning

confidence: 99%

“…However, designing these abstractions is non-intuitive and non-trivial and requires a domain expert. Most related approaches require hand-coded abstractions (Garrett, Lozano-Pérez, and Kaelbling 2020;Shah et al 2020) or require experience in the test domain (Bagaria and Konidaris 2020) to learn abstractions.…”

Section: Introductionmentioning

confidence: 99%

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Learning Neuro-Symbolic Abstractions for Robot Planning and Learning

Shah

2024

AAAI

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Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. On the other hand, non-hierarchical robot planning approaches fail to compute solutions for complex tasks that require reasoning over a long horizon. My research addresses these problems by proposing an approach for learning abstractions and developing hierarchical planners that efficiently use learned abstractions to boost robot planning performance and provide strong guarantees of reliability.

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Section: Stochastic Task and Motion Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Neuro-Symbolic Abstractions for Robot Planning and Learning

Shah

2024

AAAI

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“…Decision-theoretic task planning methods, and specifically Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs) are the most prevalent approaches for tackling various types of uncertainty in TAMP formulations (e.g., Şucan and Kavraki, 2012 ; Kaelbling and Lozano-Pérez, 2013 ; Hadfield-Menell et al, 2015 ). A recent work includes an anytime algorithm of TAMP generating policies for handling multiple execution-time contingencies using MDP-based modeling of actions which corresponds to an infinite set of motion planning problems ( Shah et al, 2020 ).…”

Section: Analysis Of the State-of-the-artmentioning

confidence: 99%

Combining Task and Motion Planning: Challenges and Guidelines

2021

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Combined Task and Motion Planning (TAMP) is an area where no one-fits-all solution can exist. Many aspects of the domain, as well as operational requirements, have an effect on how algorithms and representations are designed. Frequently, trade-offs have to be made to build a system that is effective. We propose five research questions that we believe need to be answered to solve real-world problems that involve combined TAMP. We show which decisions and trade-offs should be made with respect to these research questions, and illustrate these on examples of existing application domains. By doing so, this article aims to provide a guideline for designing combined TAMP solutions that are adequate and effective in the target scenario.

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“…Such algorithms occur frequently in time-critical robotic applications and they raise decision-theoretic problems whose objective is to determine when to stop the computation, that is, stop improving the solution, and take action. In this work, we specifically focus on a class of anytime motion planning algorithms [2]- [4]. The goal of motion planning (in Figure 1) is to find a collision-free path from the initial configuration to the goal configuration such that the robot does not collide with any obstacles.…”

Section: Introductionmentioning

confidence: 99%

Learning When to Quit: Meta-Reasoning for Motion Planning

Sung¹,

Kaelbling²,

Lozano-Pérez³

2021

Preprint

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Anytime motion planners are widely used in robotics. However, the relationship between their solution quality and computation time is not well understood, and thus, determining when to quit planning and start execution is unclear. In this paper, we address the problem of deciding when to stop deliberation under bounded computational capacity, so called meta-reasoning, for anytime motion planning. We propose data-driven learning methods, model-based and model-free meta-reasoning, that are applicable to different environment distributions and agnostic to the choice of anytime motion planners. As a part of the framework, we design a convolutional neural network-based optimal solution predictor that predicts the optimal path length from a given 2D workspace image. We empirically evaluate the performance of the proposed methods in simulation in comparison with baselines.

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Anytime Integrated Task and Motion Policies for Stochastic Environments

Cited by 8 publications

References 20 publications

Learning Neuro-Symbolic Abstractions for Robot Planning and Learning

Learning Neuro-Symbolic Abstractions for Robot Planning and Learning

Combining Task and Motion Planning: Challenges and Guidelines

Learning When to Quit: Meta-Reasoning for Motion Planning

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