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Task and Motion Planning for Robots ...

Ding, Yan.

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Task and Motion Planning for Robots in Open Worlds.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Task and Motion Planning for Robots in Open Worlds./
Author:	Ding, Yan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	160 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
Contained By:	Dissertations Abstracts International85-12B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30994302
ISBN:	9798383097182

Task and Motion Planning for Robots in Open Worlds.
Ding, Yan.

Task and Motion Planning for Robots in Open Worlds. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 160 p.

Source: Dissertations Abstracts International, Volume: 85-12, Section: B.

Thesis (Ph.D.)--State University of New York at Binghamton, 2024.

To complete long-horizon tasks, real-world robots generally need to plan at both task and motion levels. Task-level planning involves generating a sequence of symbolic actions in a discrete space for long-term goals. In contrast, motion-level planning implements these actions by computing trajectories in a continuous space. Real-world robots usually operate within the context of "open-world" environments, characterized by their unpredictable and dynamic nature. Existing task and motion planning frameworks, however, tend to focus on closed-world settings, which assume the robot is provided with complete world knowledge. This discrepancy poses several challenges. At times, robots may receive underspecified requests from users. In these instances, robots should understand the requests and generate plans that meet user preferences. Additionally, robots might encounter new objects during task execution. Adapting their plans in response to these new objects is crucial. Moreover, robots must manage uncertainties from other agents, such as humans or robots, in open-world settings. Despite having a plan, they may face unexpected situations that hinder task completion.The primary contribution of this research is to develop task and motion planning algorithms and systems that are efficient, feasible, and adaptable for robots operating in open-world environments. This dissertation comprises several studies. I begin by introducing TMPUD, a task and motion planning framework for urban autonomous driving under full observability. TMPUD is designed to handle the uncertainty from other agents, aiming to enhance the safety and efficiency of urban driving. In contrast, the second framework, GLAD, operates under partial observability. The third framework, TMOC, focuses on planning with novel objects. This framework incorporates a physics engine to ground objects, learn about their physical attributes, and improve task-motion planning skills based on gathered learning experiences. Next, I discuss LLM-GROP, a method that leverages commonsense knowledge for planning to handle underspecified user requests in tasks involving multi-object rearrangement. Finally, I present COWP, a system that combines task-oriented commonsense knowledge derived from Large Language Models (LLMs) with classical AI planning methods to effectively handle unforeseen situations.

ISBN: 9798383097182Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Large Language Models

Task and Motion Planning for Robots in Open Worlds.
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520 $a To complete long-horizon tasks, real-world robots generally need to plan at both task and motion levels. Task-level planning involves generating a sequence of symbolic actions in a discrete space for long-term goals. In contrast, motion-level planning implements these actions by computing trajectories in a continuous space. Real-world robots usually operate within the context of "open-world" environments, characterized by their unpredictable and dynamic nature. Existing task and motion planning frameworks, however, tend to focus on closed-world settings, which assume the robot is provided with complete world knowledge. This discrepancy poses several challenges. At times, robots may receive underspecified requests from users. In these instances, robots should understand the requests and generate plans that meet user preferences. Additionally, robots might encounter new objects during task execution. Adapting their plans in response to these new objects is crucial. Moreover, robots must manage uncertainties from other agents, such as humans or robots, in open-world settings. Despite having a plan, they may face unexpected situations that hinder task completion.The primary contribution of this research is to develop task and motion planning algorithms and systems that are efficient, feasible, and adaptable for robots operating in open-world environments. This dissertation comprises several studies. I begin by introducing TMPUD, a task and motion planning framework for urban autonomous driving under full observability. TMPUD is designed to handle the uncertainty from other agents, aiming to enhance the safety and efficiency of urban driving. In contrast, the second framework, GLAD, operates under partial observability. The third framework, TMOC, focuses on planning with novel objects. This framework incorporates a physics engine to ground objects, learn about their physical attributes, and improve task-motion planning skills based on gathered learning experiences. Next, I discuss LLM-GROP, a method that leverages commonsense knowledge for planning to handle underspecified user requests in tasks involving multi-object rearrangement. Finally, I present COWP, a system that combines task-oriented commonsense knowledge derived from Large Language Models (LLMs) with classical AI planning methods to effectively handle unforeseen situations.
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