Calendar
Abstract:
Modeling and control problems generally get harder the more Degrees of Freedom (DoF) are involved, suggesting that moving with many legs or grasping with many fingers should be difficult to describe. In this talk I will show how insights from the theory of geometric mechanics, a theory developed about 20-30 years ago by Marsden, Ostrowski, and Bloch, might turn that notion on its head. I will motivate the claim that when enough legs contact the ground, the complexity associated with momentum is gone, to be replaced by a problem of slipping contacts. In this regime, equations of motion are replaced by a “connection” which is both simple to estimate in a data driven form, and easy to simulate by adopting some non-conventional friction models. The talk will contain a brief intro to geometric mechanics, and consist mostly of results showing that: (i) this class of models is more general than may seem at first; (ii) they can be used for very rapid hardware in the loop gait optimization of both simple and complex robots; (iii) they motivate a simple motion model that fits experimental results remarkably well. If successful, this research agenda could improve motion planning speeds for multi-contact robotic systems by several orders of magnitude, and explain how simple animals can move so well with many limbs.
Bio:
Shai Revzen is an Assistant Professor in the University of Michigan, Ann Arbor. His primary appointment is in the department of Electrical Engineering and Computer Science in the College of Engineering. He holds a courtesy faculty appointment in the Department of Ecology and Evolutionary Biology, and is an Assistant Director of the Michigan Robotics Institute. He received his PhD in Integrative Biology from the University of California at Berkeley, and an M.Sc. in Computer Science from the Hebrew University in Jerusalem. In addition to his academic work, Shai was Chief Architect R&D of the convergent systems division of Harmonic Lightwaves (HLIT), and a co-founder of Bio-Systems Analysis, a biomedical technology start-up. As principal investigator of the Biologically Inspired Robotics and Dynamical Systems (BIRDS) lab, Shai sets the research agenda and approach of the lab: a focus on fundamental science, realizing its transformative influence on robotics and other technology. Work in the lab is equally split between robotics, mathematics, and biology.
Abstract:
TBA
Bio:
TBA
Peter A. Sheppard – Sr. Intellectual Property Manager Johns Hopkins Technology Ventures
“Intellectual Property Primer For Conflict of Interest Training.”
Laura M. Evans – Senior Policy Associate, Director, Homewood IRB
“Conflicts of Interest: Identification, Review, and Management.”
Abstract:
Causal inference from observational data is a vital problem, but it comes with strong assumptions. Most methods require that we observe all confounders, variables that affect both the causal variables and the outcome variables. But whether we have observed all confounders is a famously untestable assumption. We describe the deconfounder, a way to do causal inference with weaker assumptions than the classical methods require.
How does the deconfounderwork? While traditional causal methods measure the effect of a single cause on an outcome, many modern scientific studies involve multiple causes, different variables whose effects are simultaneously of interest. The deconfounderuses the correlation among multiple causes as evidence for unobserved confounders, combining unsupervised machine learning and predictive model checking to perform causal inference.We demonstrate the deconfounderon real-world data and simulation studies, and describe the theoretical requirements for the deconfounderto provide unbiased causal estimates.
Bio:
David Bleiis a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. He studies probabilistic machine learning, including its theory, algorithms, and application. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), BlavatnikFaculty Award (2013), ACM-Infosys Foundation Award (2013), a Guggenheim fellowship (2017), and a Simons Investigator Award (2019). He is the co-editor-in-chief of the Journal of Machine Learning Research.He is a fellow of the ACM and the IMS.
[*] https://arxiv.org/abs/1805.06826
Abstract:
Humans interact with other humans or the world through information from various channels including vision, audio, language, haptics, etc. To simulate intelligence, machines require similar abilities to process and combine information from different channels to acquire better situation awareness, better communication ability, and better decision-making ability. In this talk, we describe three projects. In the first study, we enable a robot to utilize both vision and audio information to achieve better user understanding. Then we use incremental language generation to improve the robot’s communication with a human. In the second study, we utilize multimodal history tracking to optimize policy planning in task-oriented visual dialogs. In the third project, we tackle the well-known trade-off between dialog response relevance and policy effectiveness in visual dialog generation. We propose a new machine learning procedure that alternates from supervised learning and reinforcement learning to optimum language generation and policy planning jointly in visual dialogs. We will also cover some recent ongoing work on image synthesis through dialogs, and generating social multimodal dialogs with a blend of GIF and words.
Bio:
Zhou Yu is an Assistant Professor at the Computer Science Department at UC Davis. She received her PhD from Carnegie Mellon University in 2017. Zhou is interested in building robust and multi-purpose dialog systems using fewer data points and less annotation. She also works on language generation, vision and language tasks. Zhou’s work on persuasive dialog systems received an ACL 2019 best paper nomination recently. Zhou was featured in Forbes as 2018 30 under 30 in Science for her work on multimodal dialog systems. Her team recently won the 2018 Amazon Alexa Prize on building an engaging social bot for a $500,000 cash award.
Abstract:
In this talk, we present recent progress towards developing learning-based control strategies for the design of safe and robust autonomous systems. Our approach is to recognize that machine learning algorithms produce inherently uncertain estimates or predictions, and that this uncertainty must be explicitly quantified (e.g., using non-asymptotic guarantees of contemporary high-dimensional statistics) and accounted for (e.g., using robust control/optimization) when designing safety critical systems. We focus on the optimal control of unknown systems, and show that by integrating modern tools from high-dimensional statistics and robust control, we can provide, to the best of our knowledge, the first end-to-end finite data robustness, safety, and performance guarantees for learning and control. We also briefly highlight how these ideas can be extended to the large-scale distributed setting by similarly integrating tools from structured linear inverse problems with tools from distributed robust and optimal control. As a whole, these results provide a rigorous and contemporary perspective on safe reinforcement learning as applied to continuous control. We conclude with our vision for a general theory of safe learning and control, with the ultimate goal being the design of robust and high performing data-driven autonomous systems.
Bio:
Nikolai Matni is an assistant professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania, where he is also a member of the GRASP Lab, PRECISE Center, and Applied Mathematics and Computational Science graduate group. Prior to joining Penn, Nikolai was a postdoctoral scholar in EECS at UC Berkeley. He has also held a position as a postdoctoral scholar in the Computing and Mathematical Sciences at Caltech. He received his Ph.D. in Control and Dynamical Systems from Caltech in June 2016. He also holds B.A.Sc. and M.A.Sc. in Electrical Engineering from the University of British Columbia, Vancouver, Canada. His research interests broadly encompass the use of learning, optimization, and control in the design and analysis of safety-critical and data-driven cyber-physical systems. Nikolai was awarded the IEEE CDC 2013 Best Student Paper Award (first ever sole author winner) and the IEEE ACC 2017 Best Student Paper Award (as co-advisor).
