WAFR 2016 San Francisco, Dec. 18-20

Erik Demaine

Massachusetts Institute of Technology

Replicators, Transformers, and Robot Swarms: Science Fiction through Geometric Algorithms

Abstract: Science fiction is a great inspiration for science. How can we build reconfigurable robots like Transformers or Terminator 2? How can we build Star Trek-style replicators that duplicate or mass-produce a given shape at the nano scale? How can we orchestrate the motion of a large swarm of robots? Recently we've been exploring possible answers to these questions through computational geometry, in the settings of reconfigurable robots (both modular and folding robots that can become any possible shape), robot swarms (which may be so small and simple that they have no identity), and self-assembly (building computers and replicators out of DNA tiles).

Bio: Erik Demaine is a Professor in Computer Science at the Massachusetts Institute of Technology. Demaine's research interests range throughout algorithms, from data structures for improving web searches to the geometry of understanding how proteins fold to the computational difficulty of playing games. He received a MacArthur Fellowship (2003) as a "computational geometer tackling and solving difficult problems related to folding and bending--moving readily between the theoretical and the playful, with a keen eye to revealing the former in the latter". Erik cowrote a book about the theory of folding, together with Joseph O'Rourke (Geometric Folding Algorithms, 2007), and a book about the computational complexity of games, together with Robert Hearn (Games, Puzzles, and Computation, 2009). Together with his father Martin, his interests span the connections between mathematics and art, including curved origami sculptures in the permanent collections of the Museum of Modern Art in New York, and the Renwick Gallery in the Smithsonian.

Lydia Kavraki

Rice University

20 Years of Sampling Robot Motion

Abstract: Sampling-based planners for robot motion planning were discovered about twenty years ago and have had a major impact in the way we perceive and practice robot planning today. This talk will discuss the advantages and shortcomings of sampling-based planners, and the new challenges that we face when planning for complex robotic systems. The talk will also present advances that robotics-inspired planners have enabled in computational biology and in the design of new therapeutics.

Bio: Lydia Kavraki is the Noah Harding Professor of Computer Science at Rice University. She also holds a joint appointment at the Department of Bioengineering at Rice. Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University working with Jean-Claude Latombe. She splits her research efforts working in robotics and computational structural biology. In robotics she studies task and motion planning, manipulation, and formal methods applied to robotics. In computational structural biology she introduces new methodologies for the analysis of structure and function of molecules from small proteins to viral capsids. Information about Kavraki and her work can be found at www.kavrakilab.org

John Canny

University of California, Berkeley

A Guided Tour of Computer Vision, Robotics, Algebra, and HCI

Abstract: This walk will be a fast, firmly-shepherded tour of work on vision, robotics, algebra and HCI. It will cover several decades of work, and draw connections between earlier work and the state-of-the-art now. Many of these problems were challenges in the definition phase - figuring out the real problem to be solved, and then finding the right methods to solve it. Several of them required digging deeply into other disciplines to discover acctionable principles. In all cases they involved trying new things from the great self-service counter of ideas. Some highlights include mapping high-dimensional sets, moving sets of objects by shaking, flying robots, 3D video and language-learning games. I'll close with recent work on scalable machine learning and deep learning. Much of the work to date in ML and DL is framed as an optimization problem. In ongoing work, we are exploring an alternative framing as Monte-Carlo simulation.

Bio: John Canny is a professor in computer science at UC Berkeley. He is an ACM dissertation award winner, a Packard Fellow, an Okawa Foundation Fellow and currently holds an INRIA International Chair. He works on large-scale machine learning and deep learning. He previously worked in computer vision, robotics, computational geometry, algebra, human-computer interaction and computer-aided learning. Since 2002, he has been developing and deploying behavioral modeling systems in industry. He designed and protyped production systems for Overstock.com, Yahoo, Ebay, and Quantcast, and is currently a visiting scientist at Google.

Dan Halperin

Tel Aviv University

From Piano Movers to Piano Printers: Computing and Using Minkowski Sums

Abstract: The Minkowski sum of two sets P and Q in Euclidean space is the result of adding every point in P to every point in Q. Minkowski sums constitute a fundamental tool in geometric computing, used in a large variety of domains including motion planning, solid modeling, assembly planning, 3d printing and many more. At the same time they are an inexhaustible source of intriguing mathematical and computational problems. We survey results on the structure, complexity, algorithms, and implementation of Minkowski sums in two and three dimensions. We also describe how Minkowski sums are used to solve problems in an array of applications, and primarily in robotics and automation.

Bio: Dan Halperin received his Ph.D. in Computer Science from Tel Aviv University. He then spent three years at the Computer Science Robotics Laboratory at Stanford University. In 1996 he joined the Department of Computer Science at Tel Aviv University, where he is currently a full professor and for two years was the department chair. Halperin's main field of research is Computational Geometry and its Applications. A major focus of his work has been in research and development of robust geometric software, principally as part of the CGAL project and library. The application areas he is interested in include robotics and automated manufacturing, and algorithmic motion planning. Halperin was named an IEEE Fellow in 2015. http://acg.cs.tau.ac.il/danhalperin