The Robotics Colloquium features talks by invited and local researchers on all aspects of robotics, including control, perception, machine learning, mechanical design, and interaction. The colloquium is held Fridays between 1:30-2:30pm (virtually over Zoom - details will be sent over the mailing list). Special seminars outside this schedule are indicated below.
If you would like to give a talk in upcoming Robotics Colloquia, please contact Karthik Desingh. If you would like to get regular email announcements and reminders about the robotics colloquium speakers, please sign up for the Robotics@UW mailing list.
Spring 2021 Organizers: Karthik Desingh, Dieter Fox, Maya Cakmak, Siddhartha S. Srinivasa
Abstract: A wide range of applications can benefit from robots acquiring manipulation skills by interaction with humans. In this presentation, I will discuss the challenges that such learning process encompasses, including representations for manipulation skills that can exploit the structure and geometry of the acquired data in an efficient way, the development of optimal control strategies that can exploit variations in manipulation skills, and the development of intuitive interfaces to acquire meaningful demonstrations. From a machine learning perspective, the core challenge is that robots can only rely on a small number of demonstrations. The good news is that we can exploit bidirectional human-robot interaction as a way to collect better data. We can also rely on various structures that remain the same within a wide range of robotic tasks. Such structures include geometrical aspects, by extending learning strategies that have been originally developed for standard Euclidean space to Riemannian manifolds. In robotics, these manifolds include orientation, manipulability ellipsoids, graphs and subspaces. Another type of structure that we study relates to the organization of data as multidimensional arrays (also called tensors). These data appear in various robotic tasks, either as the natural organization of sensorimotor data (tactile arrays, images, kinematic chains), or as the result of preprocessing steps (moving time windows, covariance features). Tensor factorization techniques (also called tensor methods or multilinear algebra) can be used to learn from only few tensor datapoints, by exploiting the multidimensional nature of the data. Another key challenge in robot skill acquisition is to link the learning aspects to the control aspects. Optimal control provides a framework that allows us to take into account the possible variations of a task, the uncertainty of sensorimotor information, and the movement coordination patterns, by relying on well grounded control techniques such as linear quadratic tracking, differential dynamic programming, and their extensions to model predictive controllers. The formulation draws explicit links with learning techniques, as we can recast these techniques as Gauss-Newton optimization problems formulated at trajectory level (in both control space and state space), which facilitates the links to probabilistic approaches.
Biography: Dr Sylvain Calinon is a Senior Researcher at the Idiap Research Institute (https://idiap.ch), heading the Robot Learning & Interaction group. He is also a Lecturer at the Ecole Polytechnique Federale de Lausanne (EPFL). From 2009 to 2014, he was a Team Leader at the Italian Institute of Technology. From 2007 to 2009, he was a Postdoc in the Learning Algorithms and Systems Laboratory, EPFL, where he obtained his PhD in 2007. His research interests cover robot learning, human-robot collaboration and optimal control.
Abstract: Robots for minimally-invasive surgery such as steerable needles and concentric-tube robots have the potential to dramatically alter the way common medical procedures are performed. They can decrease patient-recovery time, speed healing and reduce scarring. However, manually controlling such devices is highly unintuitive and automatic planning methods are in need. For the automation of such medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the motion-planning algorithms involved in procedure automation. In this talk I will survey some of the recent work I have been involved in where, together with colleagues at UNC, UW and UoU, we developed efficient and effective planning capabilities for medical robots that provide provable guarantees on various planner attributes.
Biography: Oren Salzman is an assistant Professor at the Computer Science department at the Technion - Israel Institute of Technology. His research focuses on revisiting classical computer science algorithms, tools and paradigms to address the computational challenges that arise when planning motions for robots. Combining techniques from diverse domains such as computational geometry, graph theory and machine learning, he strives to provide efficient algorithms with rigorous analysis for robot systems with many degrees of freedom moving in tight quarters. Oren completed a PhD in the School of Computer Science at Tel Aviv University under the supervision of Prof. Dan Halperin. He then continued his studies as a postdoctoral researcher at Carnegie Mellon University working with Siddhartha Srinivasa and Maxim Likhachev and as a research scientist at the National Robotics Engineering Center (NREC). Oren has published over forty peer-reviewed conference and journal papers. He has received the best paper and best student paper in ICAPS 18 and ICAPS 19, respectively.
Abstract: In this talk I will describe how formal methods such as synthesis – automatically creating a system from a formal specification – can be leveraged to design robots, explain and provide guarantees for their behavior, and even identify skills they might be missing. I will discuss the benefits and challenges of synthesis techniques and will give examples of different robotic systems including modular robots, swarms and robots interacting with people.
Biography: Hadas Kress-Gazit is a Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University. She received her Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania in 2008 and has been at Cornell since 2009. Her research focuses on formal methods for robotics and automation and more specifically on synthesis for robotics – automatically creating verifiable robot controllers for complex high-level tasks. Her group explores different types of robotic systems including modular robots, soft robots and swarms and synthesizes (pun intended) ideas from different communities such as robotics, formal methods, control, hybrid systems and computational linguistics. She is an IEEE fellow and has received multiple awards for her research, teaching and advocacy for groups traditionally underrepresented in STEM. She lives in Ithaca with her partner and two kids.
Abstract: Current state-of-the-art CNNs localize rare object categories in internet photos, yet, they miss basic facts that a two-year-old has mastered: that objects have 3D extent, they persist over time despite changes in the camera view, they do not 3D intersect, and others. We will discuss models that learn to map 2D and 2.5D images and videos into amodal completed 3D feature maps of the scene and the objects in it by predicting views. We will show the proposed models learn object permanence, have objects emerge in 3D without human annotations, support grounding of language in 3D visual simulations, and learn object dynamics that generalize across scene arrangements and camera placements.
Biography: Katerina Fragkiadaki is an Assistant Professor in the Machine Learning Department in Carnegie Mellon University. She received her Ph.D. from University of Pennsylvania and was a postdoctoral fellow in UC Berkeley and Google research after that. Her work is on learning visual representations with little supervision and on combining spatial reasoning in deep visual learning. Her group develops algorithms for mobile computer vision, learning of physics and common sense for agents that move around and interact with the world. Her work has been awarded with a best Ph.D. thesis award, an NSF CAREER award, AFOSR YIP award, Google, TRI, Amazon and Sony faculty research awards.
Abstract: In the last few years, the ability for robots to understand and operate in the world around them has advanced considerably. Examples include the growing number of self-driving car systems, the considerable work in robot mapping, and the growing interest in home and service robots. However, one limitation is that robots most often reason and plan using very geometric models of the world, such as point features, dense occupancy grids and action cost maps. To be able to plan and reason over long length and timescales, as well as planning more complex missions, robots need to be able to reason about abstract concepts such as landmarks, segmented objects and tasks (among other representations). I will talk about recent work in joint reasoning about semantic representations and physical representations and what these joint representations mean for planning and decision making.
Biography: Nicholas Roy is the Bisplinghoff Professor of Aeronautics & Astronautics and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology. He received his B.Sc. in Physics and Cognitive Science and his M.Sc. in Computer Science from McGill University. He received his Ph. D. in Robotics from Carnegie Mellon University. He has made research contributions to planning under uncertainty, machine learning, human-computer interaction and aerial robotics. He is currently the director of the Bridge in MIT's Quest for Intelligence.
Details of previous Robotics Colloquiua can be found here.