Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.


A Stochastic Approach to Robotic Self-Assembly

Systems of autonomous robots manipulating and assembling raw materials could be used to assemble and reconfigure a variety of useful structures. Practically, such systems present a number of challenges: communication and sensing among many robots are local and likely prone to failures and delays; robots and other components may fail; the behavior of supply chains may be random. Essentially, stochasticity dominates -- presenting unique engineering problems. At the same time, good algorithms can exploit stochasticity to improve performance and flexibility. Here, we describe how to specify and program stochastic self-assembly behaviors using a formal programming language. In particular, we show how to express transport, assembly, load-balancing, distributed estimation, fault-tolerance and other tasks with simple sub-behaviors that can be composed into larger multi-functional programs. We demonstrate the approach on a variety of testbeds, including the Factory Floor Testbed, a joint project with Mark Yim's group at U. Penn, wherein large numbers of simple robot arms construct and "digest" structures from simple mechanical components.

Type of Seminar:
IfA Seminar
Prof. Eric Klavins
Department of Electrical Engineering, University of Washington, Seattle
Jul 18, 2011   17:15

ETZ E6, Gloriastrasse 35
Contact Person:

John Lygeros
File Download:

Request a copy of this publication.
Biographical Sketch:
Professional preparation: • San Francisco State University, Computer Science, B.S, 1996 • University of Michigan, Computer Science and Engineering, M.S., 1998 • University of Michigan, Computer Science and Engineering, Ph.D., 2001 • California Inst. of Technology, Control and Dynamical Systems, Postdoc., 2001-03.
Primary appointments: • Associate Professor, Electrical Engineering, University of Washington, 2009 – present. • Assistant Professor, Electrical Engineering, University of Washington, 2003 – 2009. • Postdoctoral Research Scholar, Joint appointment in Computer Science and Control and Dynamical Systems, California Institute of Technology, 2001-2003. • Research/Teaching Assistant, University of Michigan, 1997-2001.

Research description: From Robotics to Synthetic Biology Eric Klavins studies the programmability of self-organizing processes. His research started in robotics, where he developed new algorithms based on coupled oscillators to control legged locomotion of insect-like robots and manufacturing processes. This led to methods to control multirobot swarms based, on loosely coupled concurrent programs, and eventually to self-assembling robot systems. In addition to investigating the programming and control of such systems, Klavins’ lab has built several robotic testbeds to demonstrate the ideas. These systems are centered on stochastically interacting robotic particles that can form and break bonds according to their programming. The programmed stochastic processes that arise are very similar to control stochastic chemical reactions such as might be found in living cells.