October 03

Add to Calendar 2017-10-03 11:00:00 2017-10-03 12:00:00 America/New_York A Control and Estimation Framework for Robotic Swarms in Uncertain Environments Abstract: Robotic “swarms” comprising tens to thousands of robots have the potential to greatly reduce human workload and risk to human life. In many scenarios, the robots will lack global localization, prior data about the environment, and reliable communication, and they will be restricted to local sensing and signaling. We are developing a rigorous control and estimation framework for swarms that are subject to these constraints. This framework will enable swarms to operate largely autonomously, with user input consisting only of high-level directives. In this talk, I describe our work on various aspects of the framework, including scalable strategies for coverage, mapping, scalar field estimation, and cooperative manipulation. We use stochastic and deterministic models from chemical reaction network theory and fluid dynamics to describe the robots’ roles, state transitions, and motion at both the microscopic (individual) and macroscopic (population) levels. We also employ techniques from algebraic topology, nonlinear control theory, and optimization, and we model analogous behaviors in ant colonies to identify robot controllers that yield similarly robust performance. We are validating our framework on small mobile robots, called “Pheeno,” that we have designed to be low-cost, customizable platforms for multi-robot research and education.Bio: Spring Berman is an assistant professor of Mechanical and Aerospace Engineering at Arizona State University (ASU), where she directs the Autonomous Collective Systems (ACS) Laboratory. She received the B.S.E. degree in Mechanical and Aerospace Engineering from Princeton University in 2005 and the Ph.D. degree in Mechanical Engineering and Applied Mechanics from the University of Pennsylvania (GRASP Laboratory) in 2010. From 2010 to 2012, she was a postdoctoral researcher in Computer Science at Harvard University. Her research focuses on controlling swarms of resource-limited robots with stochastic behaviors to reliably perform collective tasks in realistic environments. She was a recipient of the 2014 DARPA Young Faculty Award and the 2016 ONR Young Investigator Award. She currently serves as the associate director of the newly established ASU Center for Human, Artificial Intelligence, and Robotic Teaming. 32-G449 Patil/Kiva Belfer sarah_donahue@hks.harvard.edu

November 28

Add to Calendar 2017-11-28 11:00:00 2017-11-28 12:00:00 America/New_York Robots at Sea AbstractUnderwater robotics is undergoing a transformation. Advances in AI and machine learning are enabling a new generation of underwater robots to make intelligent decisions (where to sample? how to navigate?) by reasoning about their environment (what is the shipping and water forecast?). At USC, we are engaged in a long-term effort to explore ideas and develop algorithms that will lead to persistent, autonomous underwater robots. In this talk, I will discuss some of our recent results focusing on two problems in adaptive sampling: underwater change detection and biological sampling. Time permitting; I will also present our work on hazard avoidance, allowing underwater robots to operate in regions where there is substantial ship traffic.BioGaurav S. Sukhatme is the Fletcher Jones Professor of Computer Science and Electrical Engineering at the University of Southern California (USC). He currently serves as the Executive Vice Dean of the USC Viterbi School of Engineering. His research is in networked robots with applications to aquatic robots and on-body networks. Sukhatme has published extensively in these areas and served as PI on numerous federal grants. He is Fellow of the IEEE and a recipient of the NSF CAREER and the Okawa foundation research awards. He is one of the founders of the RSS conference, serves on the RSS Foundation Board, and has served as program chair of three major robotics conferences (ICRA, IROS and RSS). He is the Editor-in-Chief of the Springer journal Autonomous Robots. 32-G449 Belfer sarah_donahue@hks.harvard.edu

December 05

December 15

Add to Calendar 2017-12-15 14:00:00 2017-12-15 15:00:00 America/New_York Dirty Data, Robotics, and Artificial Intelligence Abstract:Large training datasets have revolutionized AI research, but enabling similar breakthroughs in other fields, such as Robotics, requires a new understanding of how to acquire, clean, and structure emergent forms of large-scale, unstructured sequential data. My talk presents a systematic approach to handling such dirty data in the context of modern AI applications. I start by introducing a statistical formalization on data cleaning in this setting including research on: (1) how common data cleaning operations affect model training, (2) how data cleaning programs can be expected to generalize to unseen data, (3) and how to prioritize limited human intervention in rapidly growing datasets. Then, using surgical robotics as a motivating example, I present a series of robust Bayesian models for automatically extracting hierarchical structure from highly varied and noisy robot trajectory data facilitating imitation learning and reinforcement learning on short, consistent sub-problems. I present how the combination of clean training data and structured learning tasks enables learning highly accurate control policies in tasks ranging from surgical cutting to debridement.Bio:Sanjay Krishnan is a Computer Science PhD candidate in the RISELab and in the Berkeley Laboratory for Automation Science and Engineering at UC Berkeley. His research studies problems on the intersection of database theory, machine learning, and robotics. Sanjay's work has received a number of awards including the 2016 SIGMOD Best Demonstration award, 2015 IEEE GHTC Best Paper award, and Sage Scholar award. https://www.ocf.berkeley.edu/~sanjayk/ Star (32-D463) Belfer sarah_donahue@hks.harvard.edu