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Events for the 4th week of May
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PhD Defense - Luenin Barrios
Thu, May 25, 2017 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Luenin Barrios
Committee: Wei-Min Shen (chair), Stephan Haas, Aiichiro Nakano.
Title: Simultaneous Center of Mass Estimation and Foot Placement Selection in Complex Planar Terrains for Legged Architectures
Time: Thursday, May 25 at 10am
Room: SAL 213
Abstract:
Center of Mass (CoM) path planning and foot placement selection in complex and rough terrains remains an important goal in the development of motion plans for legged robots. Precise CoM measurements and percipient foot placements are essential in understanding the behavior of a system, for example in gait selection or in extreme locomotion maneuvers. However, operating and maneuvering in difficult terrains has remained a challenging problem due to the diversity of environments and the complex interplay of foot placements and CoM motions. These locomotion maneuvers involve complex forces and movements that make analysis of CoM behavior a challenging task. Nevertheless, understanding CoM dynamics remains pivotal in locomotion planning for both humans and robots. Indeed, the critical element in robot and human motion planning revolves around the ability to accurately measure and describe the CoM. But given the cyclopean space of natural terrains available and the large number of kinematic shapes and sizes possible, the question arises: Is it conceivable to create a generalized framework for CoM construction and estimation with optimal foot placement selection that incorporates the large variety of kinematic architectures and terrains? The work described in this research addresses this issue by presenting a generalized geometric framework from which accurate CoM estimates are produced for the case of bipedal locomotion in complex planar terrains. This framework allows for the simultaneous treatment of CoM estimation and foot placement selection in legged architectures in an efficient and straightforward manner. This is a marked change from current methods for CoM position estimation that rely heavily on expensive and ungainly tools, for example force plates and motion capture video. These render CoM analysis impractical and time consuming and serve as an impediment to understanding locomotion maneuvers in uneven terrains. To tackle these challenges, this work proposes a reliable geometric approach for CoM estimation that delivers accurate CoM behavior in complex planar terrains. The geometric approach depends only on terrain geometry information and essential kinematic data of the moving body. Using this key information in conjunction with an Optimized Geometric Hermite (OGH) curve, a model is developed that produces accurate CoM position and phase space behavior. This phase space behavior is simultaneously optimized during CoM estimation to find candidate foot locations that produce an overall plan with minimum energy. This provides a way to synthesize complex maneuvers in rough terrains and to develop accurate CoM estimates and foot placement plans. Various human case studies were analyzed to validate the effectiveness of the approach. The results show that for natural walking over complex planar terrains, the geometric approach generates accurate CoM path approximations and state space trajectories and is a powerful tool for understanding CoM behavior and foot placements in irregular planar terrains.
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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CNSL 2017 Conference on Nonconvex Statistical LearningFri, May 26, 2017 @ 08:00 AM - 05:00 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Multiple, Multiple
Talk Title: CNSL 2017 Conference on Nonconvex Statistical Learning
Host: Epstein Department of Industrial & Systems Engineering
More Information: CNSL2017_poster_lores.pdf
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Michele ISE
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NL Seminar-BUILDING ADAPTABLE AND SCALABLE NATURAL LANGUAGE GENERATION SYSTEMS
Fri, May 26, 2017 @ 03:00 PM - 04:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Yannis Konstas, Univ. of Washington
Talk Title: BUILDING ADAPTABLE AND SCALABLE NATURAL LANGUAGE GENERATION SYSTEMS
Series: Natural Language Seminar
Abstract: Traditionally, computers communicate with humans by converting computer readable input to human interpretable output, for example via graphical user interfaces. My research focuses on building programs that automatically generate textual output from computer-readable input. The majority of existing Natural Language Generation NLG systems use hard-wired rules or templates in order to capture the input for every different application and rely on small manually annotated corpora. In this talk, I will present a framework for building NLG systems using Neural Network architectures. The approach makes no domain specific modifications to the input and benefits from training on very large unannotated corpora. It achieves state of the art performance on a number of tasks, including generating text from meaning representations and source code. Such a system can have direct applications to intelligent conversation agents, source code assistant tools, and semantic based Machine Translation.
Biography: A postdoctoral researcher at the University of Washington, Seattle, collaborating with Prof. Luke Zettlemoyer since 2015. His main research interest focuses on the area of Natural Language Generation NLG with an emphasis on data-driven deep learning methods. He has received BSc in Computer Science from AUEB Greece in 2007, and MSc in Artificial Intelligence from the University of Edinburgh 2008. He continued his study at the University of Edinburgh and received his PhD. degree in 2014. He has previously worked as a Research Assistant at the University of Glasgow 2008, and as a postdoctoral researcher at the University of Edinburgh 2014.
Host: Marjan Ghazvininejad and Kevin Knight
More Info: http://nlg.isi.edu/nl-seminar/
Location: Information Science Institute (ISI) - 11th Flr Conf Rm # 1135, Marina Del Rey
Audiences: Everyone Is Invited
Contact: Peter Zamar
Event Link: http://nlg.isi.edu/nl-seminar/
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CNSL 2017 Conference on Nonconvex Statistical Learning
Sat, May 27, 2017 @ 08:00 AM - 05:00 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Multiple, Multiple
Talk Title: CNSL 2017 Conference on Nonconvex Statistical Learning
Host: Epstein Department of Industrial & Systems Engineering
More Information: CNSL2017_poster_lores.pdf
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Michele ISE
