Dynamic and structured scene representation for robotic manipulation
Wang, Yixuan
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https://hdl.handle.net/2142/125573
Description
Title
Dynamic and structured scene representation for robotic manipulation
Author(s)
Wang, Yixuan
Issue Date
2024-07-19
Director of Research (if dissertation) or Advisor (if thesis)
Li, Yunzhu
Driggs-Campbell, Katie
Committee Member(s)
Hajek, Bruce
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Robotic Manipulation
Robot Learning
Representation Learning
Language
eng
Abstract
Representation is an essential component of typical robotic manipulation frameworks. An ideal representation should be both efficient for computation and sufficient for downstream tasks. However, existing representations typically have fixed dimensions, which may not be optimal for different tasks. Therefore, in the first part of the thesis, we propose a dynamic representation that can dynamically adapt its dimensions to current the observation and the goal. Through various pile manipulation experiments, we demonstrate that dynamic representation can significantly improve the performance of robotic manipulation tasks compared to fixed- size representations. In the second part of the thesis, we propose another novel implicit representation, D$^3$Fields, that is 3D, semantic, and dynamic. Such a representation can be used for zero-shot generalizable rearrangement tasks, where the goal is specified by 2D images. We demonstrate the effectiveness of our D$^3$Fields through a wide range of robotic rearrangement tasks, including organizing shoes, collecting debris, and organizing office desks. Compared to state-of-the-art implicit 3D representations, such as FeatureNeRF and DistilledNeRF [1], [2], our D$^3$Fields is more computationally efficient and effective for novel scenes.
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