Data-efficient learning for manipulation, locomotion, and information gathering involving granular media and deformable objects

Zhu, Yifan

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https://hdl.handle.net/2142/121465 Copy

Description

Title

Data-efficient learning for manipulation, locomotion, and information gathering involving granular media and deformable objects

Author(s)

Zhu, Yifan

Issue Date

2023-07-10

Director of Research (if dissertation) or Advisor (if thesis)

Hauser , Kris

Doctoral Committee Chair(s)

Hauser , Kris

Committee Member(s)

• Driggs-Campbell, Katie
• Forsyth, David
• Berenson, Dmitry
• Goldberg, Ken

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• granular media
• deformable objects
• data-efficient learning
• manipulation
• locomotion
• few-shot learning

Abstract

Models of robots and how they contact the external world is traditionally built based on physics. However, such an approach is limited when the physics of certain phenomena are not well understood, when it is computationally prohibitive to solve for the equations, and when identifying equation parameters and solving conditions is challenging in the real world with partial and noisy observations. Recently, advancements in deep learning have provided a potential way to deal with this challenge, leveraging extremely flexible function approximators such as neural networks. However, the data required for many common robotics tasks could be prohibitive due to the complexity of the physics involved. This thesis aims to make progress toward addressing the issue of data efficiency for complex physics phenomena such as granular media, heterogeneous deformable objects, and acoustics of human bodies. To this end, this thesis adopts two main methodologies. First, a gray-box learning approach where learning is tightly integrated with physics, is employed to improve data efficiency. The core idea here is to decompose physics into parts that can be described by efficient analytical equations, and parts that are poorly understood or computationally heavy, which are learned from data. In this thesis, I will demonstrate different ways of combining knowledge of physics and learning to achieve data efficiency on multiple challenging problems. The second methodology aims to use meta-learning, or learning to learn, to extract useful prior knowledge from offline data on related tasks to accelerate online learning on novel tasks. I will demonstrate a novel meta-learning technique that enables a robot to use vision and very little online experience to achieve high-quality scooping actions on out-of-distribution granular terrains. We further show that these two methodologies can complement each other by demonstrating that the proposed meta-learning algorithm can improve gray-box learning for deformable objects. In addition to these two main methodologies, I also discuss my other relevant efforts in solving contact-rich robotics tasks, including automated excavation and manipulation in unstructured environments with an immersive, novice-friendly avatar robot that achieved 4-th place in the ANA XPRIZE Avatar Challenge.

Graduation Semester

2023-08

Type of Resource

Thesis

Copyright and License Information

2023 Yifan Zhu

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