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Albert H. Li
Researcher at University of California, Berkeley
Publications - 6
Citations - 378
Albert H. Li is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Optimization problem & GRASP. The author has an hindex of 4, co-authored 4 publications receiving 268 citations.
Papers
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Proceedings ArticleDOI
Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning
TL;DR: A compliant suction contact model is proposed that computes the quality of the seal between the suction cup and local target surface and a measure of the ability of thesuction grasp to resist an external gravity wrench.
Posted Content
Dex-Net 3.0: Computing Robust Robot Suction Grasp Targets in Point Clouds using a New Analytic Model and Deep Learning
TL;DR: In this article, a compliant suction contact model is proposed to compute the quality of the seal between the suction cup and local target surface and a measure of the ability of a suction grasp to resist an external gravity wrench.
Journal ArticleDOI
Inverse Statics Optimization for Compound Tensegrity Robots
Andrew P. Sabelhaus,Albert H. Li,Kimberly A. Sover,Jacob R. Madden,Andrew R. Barkan,Adrian K. Agogino,Alice M. Agogino +6 more
TL;DR: Simulations illustrate how this inverse statics optimization problem can be used for both the design and control of two different compound tensegrity applications: a spine robot and a quadruped robot built from that spine.
Posted Content
Inverse Statics Optimization for Compound Tensegrity Robots
Andrew P. Sabelhaus,Albert H. Li,Kimberly A. Sover,Jacob R. Madden,Andrew R. Barkan,Adrian K. Agogino,Alice M. Agogino +6 more
TL;DR: In this article, a static equilibrium model for compound tense-grity robots is proposed to calculate tension forces in cable-driven tense-integrity structures, and a solution is proposed as a quadratic optimization problem with practical constraints.
Journal ArticleDOI
Topology Optimization of Wing Ribs for Additive Manufacturing
TL;DR: In this paper , a lightweight wing rib structure was designed by combining topology optimization with additive manufacturing, and a deep feed-forward neural network model was proposed to perform the load prediction for the constructed wing structure incorporated with this optimized rib.