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D. Lee

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2 papers

2 author rows

ICRA Conference 2018 Conference Paper

Cartman: The Low-Cost Cartesian Manipulator that Won the Amazon Robotics Challenge

Douglas Morrison
Adam W. Tow
M. McTaggart
R. Smith
Norton Kelly-Boxall
Sean Wade-McCue
Jordan Erskine
R. Grinover

The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. This paper presents the design of our custom-built, cost-effective, Cartesian robot system Cartman, which won first place in the competition finals by stowing 14 (out of 16) and picking all 9 items in 27 minutes, scoring a total of 272 points. We highlight our experience-centred design methodology and key aspects of our system that contributed to our competitiveness. We believe these aspects are crucial to building robust and effective robotic systems.

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AAAI Conference 2011 Conference Paper

Multiple-Instance Learning: Multiple Feature Selection on Instance Representation

I-Hong Jhuo
D. Lee

In multiple-Instance Learning (MIL), training class labels are attached to sets of bags composed of unlabeled instances, and the goal is to deal with classiﬁcation of bags. Most previous MIL algorithms, which tackle classiﬁcation problems, consider each instance as a represented feature. Although the algorithms work well in some prediction problems, considering diverse features to represent an instance may provide more signiﬁcant information for learning task. Moreover, since each instance may be mapped into diverse feature spaces, encountering a large number of irrelevant or redundant features is inevitable. In this paper, we propose a method to select relevant instances and concurrently consider multiple features for each instance, which is termed as MIL-MFS. MIL-MFS is based on multiple kernel learning (MKL), and it iteratively selects the fusing multiple features for classiﬁer training. Experimental results show that the MIL-MFS combined with multiple kernel learning can signiﬁcantly improve the classiﬁcation performance.

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