Khen Elimelech

Khen Elimelech
King's College London | KCL · Department of Informatics

PhD

About

18
Publications
2,716
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78
Citations

Publications

Publications (18)
Preprint
In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as decision making in a belief space, over a possibly high-dimensional state space. Typically, to solve a decision problem, one should identify the optimal action from a set of candidates, according to some...
Conference Paper
Full-text available
Belief Space Planning (BSP) is a fundamental technique in artificial intelligence and robotics, which is widely used in the solution of problems such as online autonomous navigation and manipulation. Unfortunately , BSP is computationally demanding, especially when dealing with high-dimensional state spaces. We thus introduce PIVOT: Predic-tive Inc...
Conference Paper
Full-text available
Task planning is the problem of finding a discrete sequence of actions to achieve a goal. Unfortunately, task planning in robotic domains is computationally challenging. To address this, in our prior work, we explained how knowledge from a successful task solution can be cached for later use, as an "abstract skill." Such a skill is represented as a...
Conference Paper
Full-text available
Robotic task planning is computationally challenging. To reduce planning cost and support lifelong operation, we must leverage prior planning experience. To this end, we address the problem of extracting reusable and generalizable abstract skills from successful plan executions. In previous work, we introduced a supporting framework, allowing us, t...
Conference Paper
Full-text available
Long-horizon task planning is important for robot autonomy, especially as a subroutine for frameworks such as Integrated Task and Motion Planning. However, task planning is computationally challenging and struggles to scale to realistic problem settings. We propose to accelerate task planning over an agent's lifetime by integrating abstract strateg...
Preprint
Full-text available
Multi-Robot Task Planning (MR-TP) is the search for a discrete-action plan a team of robots should take to complete a task. The complexity of such problems scales exponentially with the number of robots and task complexity, making them challenging for online solution. To accelerate MR-TP over a system's lifetime, this work looks at combining two re...
Chapter
Task planning is the problem of finding a discrete sequence of actions to achieve a goal. Unfortunately, task planning in robotic domains is computationally challenging. To address this, in our prior work, we explained how knowledge from a successful task solution can be cached for later use, as an “abstract skill.” Such a skill is represented as a...
Chapter
Full-text available
Solving realistic robotic task planning problems is computationally demanding. To better exploit the planning effort and reduce the future planning cost, it is important to increase the reusability of successful plans. To this end, we suggest a systematic and automatable approach for plan transfer, by rethinking the plan caching procedure. Specific...
Conference Paper
Full-text available
Solving realistic robotic task planning problems is computationally demanding. To better exploit the planning effort, and reduce the future planning cost, it is important to increase the reusability of successful plans. To this end, we suggest a systematic and automatable approach for plan transfer, by rethinking the plan caching procedure. Specifi...
Article
Full-text available
In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as decision making in a belief space, over a possibly high-dimensional state space. Typically, to solve a decision problem, one should identify the optimal action from a set of candidates, according to some...
Chapter
Full-text available
Belief Space Planning (BSP) is a fundamental technique in artificial intelligence and robotics, which is widely used in the solution of problems such as online autonomous navigation and manipulation. Unfortunately, BSP is computationally demanding, especially when dealing with high-dimensional state spaces. We thus introduce PIVOT: Predictive Incre...
Thesis
Full-text available
The fundamental goal of artificial intelligence and robotics research is to allow agents and robots to autonomously plan and execute their actions. To achieve reliable and robust operation, these agents must account for uncertainty; this may derive from dynamic environments, noisy sensors, or inaccurate delivery of actions. Practically, these setti...
Preprint
Full-text available
In this work, we examine the problem of online decision making under uncertainty, which we formulate as planning in the belief space. Maintaining beliefs (i.e., distributions) over high-dimensional states (e.g., entire trajectories) was not only shown to significantly improve accuracy, but also allows planning with information-theoretic objectives,...
Article
Full-text available
In probabilistic state inference, we seek to estimate the state of an (autonomous) agent from noisy observations. It can be shown that, under certain assumptions, finding the estimate is equivalent to solving a linear least squares problem. Solving such a problem is done by calculating the upper triangular matrix R from the coefficient matrix A, us...
Chapter
Full-text available
In this paper we develop a novel paradigm to efficiently solve decision making and planning problems, and demonstrate it for the challenging case of planning under uncertainty. While conventional methods tend to optimize properties of specific problems, and sacrifice performance in order to reduce their complexity, our approach has no coupling to a...
Conference Paper
Full-text available
In this paper we develop a novel paradigm to efficiently solve decision making and planning problems, and demonstrate it for the challenging case of planning under uncertainty. While conventional methods tend to optimize properties of specific problems, and sacrifice performance in order to reduce their complexity, our approach has no coupling to a...
Conference Paper
Full-text available
In this paper we introduce a novel sparsification method for efficient decision making under uncertainty and belief space planning in high dimensional state spaces. By using a sparse version of the state's information matrix, we are able to improve the high computational cost of examination of all candidate actions. We also present an in-depth anal...
Conference Paper
Full-text available
In this paper we introduce a novel approach for efficient decision making under uncertainty and belief space planning, in high dimensional state spaces. While recently developed methods focus on sparsifying the inference process, the sparsification here is done in the context of efficient decision making, with no impact on the state inference. By i...

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