Generalizing Grasping via Shape Matching and Learning
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Zhang, WenzhengAbstract
This thesis introduces a hybrid framework for robust robotic grasp synthesis from partial point clouds. We first formulate grasping as rigid shape matching, using the gripper's Signed Distance Field (SDF) for collision avoidance and a parallelized Stochastic Gradient Descent ICP ...
See moreThis thesis introduces a hybrid framework for robust robotic grasp synthesis from partial point clouds. We first formulate grasping as rigid shape matching, using the gripper's Signed Distance Field (SDF) for collision avoidance and a parallelized Stochastic Gradient Descent ICP (SGD-ICP) for efficient optimization. We extend this with Stein Variational Gradient Descent (SVGD) to generate diverse grasp distributions. Our core innovation integrates Energy-Based Models (EBMs) with analytical ICP gradients within the SVGD pipeline, fusing learned priors with geometric optimization. Experiments show targeted dataset curation shapes the energy landscape effectively, and our synergistic approach improves grasp quality, generalization, and robustness over purely analytic or data-driven methods.
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See moreThis thesis introduces a hybrid framework for robust robotic grasp synthesis from partial point clouds. We first formulate grasping as rigid shape matching, using the gripper's Signed Distance Field (SDF) for collision avoidance and a parallelized Stochastic Gradient Descent ICP (SGD-ICP) for efficient optimization. We extend this with Stein Variational Gradient Descent (SVGD) to generate diverse grasp distributions. Our core innovation integrates Energy-Based Models (EBMs) with analytical ICP gradients within the SVGD pipeline, fusing learned priors with geometric optimization. Experiments show targeted dataset curation shapes the energy landscape effectively, and our synergistic approach improves grasp quality, generalization, and robustness over purely analytic or data-driven methods.
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Date
2026Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Computer ScienceAwarding institution
The University of SydneyShare