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Towards Generalizable Contact-Rich M...

Zhang, Xiang,

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Towards Generalizable Contact-Rich Manipulation With Robust Skill Learning, Safe Compliance Tuning, and Efficient Online Adaptation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Towards Generalizable Contact-Rich Manipulation With Robust Skill Learning, Safe Compliance Tuning, and Efficient Online Adaptation // Xiang Zhang.
Author:	Zhang, Xiang,
Description:	1 electronic resource (140 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-09, Section: B.
Contained By:	Dissertations Abstracts International86-09B.
Subject:	Robotics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31635452
ISBN:	9798310103290

Towards Generalizable Contact-Rich Manipulation With Robust Skill Learning, Safe Compliance Tuning, and Efficient Online Adaptation /
Zhang, Xiang,

Towards Generalizable Contact-Rich Manipulation With Robust Skill Learning, Safe Compliance Tuning, and Efficient Online Adaptation /Xiang Zhang. - 1 electronic resource (140 pages)

Source: Dissertations Abstracts International, Volume: 86-09, Section: B.

Robotic manipulation is increasingly transitioning from simple trajectory-following tasks to more complex, contact-rich applications such as assembly, pivoting, polishing, and wire harnessing. These tasks require robots to actively interact with objects by applying appropriate forces, posing significant challenges for automation due to the complexities of contact dynamics, safety concerns, and the need for generalization across different objects and environments. To address these challenges, this dissertation focuses on developing efficient, adaptive methodologies for enhancing robots' real-time interaction capabilities, particularly in contact-rich and force-sensitive tasks.The central theme of this dissertation is centered around three key challenges:Learning Robust Manipulation Motions for Contact-Rich Manipulation: A major challenge in robotics is to train robots to efficiently manipulate objects while making and controlling contact. Part I of the dissertation introduces techniques for improving the training process of contact-rich manipulation by incorporating human prior knowledge and optimizing skill acquisition in both simulated and real-world environments. These methods aim to reduce the sim-to-real gap, ensuring that robots can perform manipulation tasks robustly and with greater data efficiency.Compliance Gain Tuning for Safe Robot-Environment Interaction: Robots must interact safely and effectively with their environment, especially during contact-rich tasks. Part II of the dissertation addresses this by presenting algorithms for real-time compliance tuning, allowing robots to dynamically adjust their stiffness based on sensory feedback. These methods ensure that robots can safely apply the necessary forces during manipulation without compromising the safety of the task or the environment.Online Skill Adaptation for Generalizable Robot Skills: As task settings and environmental conditions change, robots must be able to generalize their learned skills to new scenarios. Part III of the dissertation combines offline skill learning with real-time online adaptation, allowing robots to adjust their manipulation skills based on live sensor data. This section emphasizes the development of policies that enable robots to perform contact-rich tasks, such as assembly and insertion, across different object types and dynamic environments.Through addressing these three core challenges, this dissertation contributes to the advancement of robust and adaptive robot manipulation techniques, ultimately pushing the boundaries of what robots can achieve in real-world settings.

English

ISBN: 9798310103290Subjects--Topical Terms:

519753
Robotics.
Subjects--Index Terms:

Autonomous systems

Towards Generalizable Contact-Rich Manipulation With Robust Skill Learning, Safe Compliance Tuning, and Efficient Online Adaptation /
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520 $a Robotic manipulation is increasingly transitioning from simple trajectory-following tasks to more complex, contact-rich applications such as assembly, pivoting, polishing, and wire harnessing. These tasks require robots to actively interact with objects by applying appropriate forces, posing significant challenges for automation due to the complexities of contact dynamics, safety concerns, and the need for generalization across different objects and environments. To address these challenges, this dissertation focuses on developing efficient, adaptive methodologies for enhancing robots' real-time interaction capabilities, particularly in contact-rich and force-sensitive tasks.The central theme of this dissertation is centered around three key challenges:Learning Robust Manipulation Motions for Contact-Rich Manipulation: A major challenge in robotics is to train robots to efficiently manipulate objects while making and controlling contact. Part I of the dissertation introduces techniques for improving the training process of contact-rich manipulation by incorporating human prior knowledge and optimizing skill acquisition in both simulated and real-world environments. These methods aim to reduce the sim-to-real gap, ensuring that robots can perform manipulation tasks robustly and with greater data efficiency.Compliance Gain Tuning for Safe Robot-Environment Interaction: Robots must interact safely and effectively with their environment, especially during contact-rich tasks. Part II of the dissertation addresses this by presenting algorithms for real-time compliance tuning, allowing robots to dynamically adjust their stiffness based on sensory feedback. These methods ensure that robots can safely apply the necessary forces during manipulation without compromising the safety of the task or the environment.Online Skill Adaptation for Generalizable Robot Skills: As task settings and environmental conditions change, robots must be able to generalize their learned skills to new scenarios. Part III of the dissertation combines offline skill learning with real-time online adaptation, allowing robots to adjust their manipulation skills based on live sensor data. This section emphasizes the development of policies that enable robots to perform contact-rich tasks, such as assembly and insertion, across different object types and dynamic environments.Through addressing these three core challenges, this dissertation contributes to the advancement of robust and adaptive robot manipulation techniques, ultimately pushing the boundaries of what robots can achieve in real-world settings.
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