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. Special seminars outside this schedule are indicated below. Refreshments are served.
If you would like to give a talk in upcoming Robotics Colloquia, please contact Maya Cakmak. 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.
Winter 2019 Organizers: Tapomayukh Bhattacharjee, Maya Cakmak, Dieter Fox, Siddhartha S. Srinivasa
Abstract: Compliant robotic systems find broad application from manufacturing to surgery. While they are amenable and soft, their planning/controls are typically slow and hard. In a typical application, over 80% of the planning time is used for just evaluating the forward model. Coupled with large deformations and continuous action spaces, the computational load can grow exponentially. To address this shortcoming, this talk will introduce the principled (Lie-symmetric) motion compensation in which search is directed along Lie Subgroups and Orbits so as to reuse previously computed paths and also to help find solutions amongst goal sets (instead of a single goal state). This is integrated via a belief-space model with non-linear biomechanical models that predict tissue motion in the presence of large strain. Its application to minimally invasive robotic surgery (a technology that affects more than 800,000 people annually) is considered. Accuracy is challenging herein as inserting a needle displaces the tissue and moves the target. Intraoperative imaging provides limited guidance and coming generation of surgical robots has a precision finer than the best imaging. Here fast forecasting informs robot design and operation by allowing for more compliance and latency.
Biography: Dr. Surya Singh is at The University of Queensland (UQ) and heads the Robotics Design Lab (RDL). His research interests lie in the design and control of compliant systems in novel (dynamic, non-Lambertian) environments that challenge traditional assumptions. Recent results include methods for fast decision making under uncertainty, sub-mm needle placement, and adaptive aids for the visually impaired. His robotics course and question-based peer review teaching software (OpenPlatypus.org) have received university teaching awards. His long-term goal is to democratize robotics and robotics education beyond its hackneyed stereotype and into the milieu.
Abstract: Entertainment is one of many applications of robotics, but it is unique in the sense that the "task" is to make people believe that they are not watching a robot, but rather a living character with personality and emotion. Pursuing speed, power, accuracy, or even efficiency often does not make much sense in such application. It therefore requires a completely different design paradigm from traditional robotics for both hardware and software. In this talk, I will discuss three elements that I believe are important for entertainment robots: motion, interaction, and design. The first part of the talk introduces various human-to-robot motion retargeting techniques for creating stylistic and expressive motions of both humanoid and non-humanoid characters. In the second part, I will demonstrate that simple, remote human-robot interaction such as playing catch and handing over an object can be engaging and entertaining by adding simple and quick reactions to human actions and events. Finally, I will introduce a few hardware prototypes of soft robots developed with the goal of realizing safe direct physical interactions including hand-shaking and hugging.
Biography: Dr. Katsu Yamane is a Senior Scientist at Honda Research Institute USA. He received his B.S., M.S., and Ph.D. degrees in Mechanical Engineering in 1997, 1999, and 2002 respectively from the University of Tokyo, Japan. Prior to joining Honda in 2018, he was a Senior Research Scientist at Disney Research, an Associate Professor at the University of Tokyo, and a postdoctoral fellow at Carnegie Mellon University. Dr. Yamane is a recipient of King-Sun Fu Best Transactions Paper Award and Early Academic Career Award from IEEE Robotics and Automation Society, and Young Scientist Award from Ministry of Education, Japan. His research interests include humanoid robot control and motion synthesis, physical human-robot interaction, character animation, and human motion simulation.
Abstract: Biological inspiration for artificial systems abounds. The science to support robotic collectives continues to emerge based on their biological inspirations, spatial swarms (e.g., fish and starlings) and colonies (e.g., honeybees and ants). Developing effective human-collective teams requires focusing on all aspects of the integrated system development. Many of these fundamental aspects have been developed independently, but our focus is an integrated development process to these complex research questions. This presentation will focus on three aspects: algorithms, transparency and resilience for collectives. Very large numbers of simplistic individuals use biologically inspired algorithms to solve more complex problems, this presentation will focus on a sequential best-of-n target selection algorithm. The size and complexity of these systems precludes a human’s ability to fully understand and communicate with each individual. Thus, transparency into the collective’s state and influencing its actions are a significant challenge that requires a close coupling with the underlying algorithms. This presentation will demonstrate a means of providing transparency and permitting influence over the collectives best-of-n decision making process. Finally, biological collectives are highly resilient to system disruptions, a feature that is an underlying expectation of robotic swarms. The questions to be addressed include how to ensure such resilience exists and how to assess this characteristic in robotic collectives.
Biography: Dr. Julie A. Adams, Professor, Associate Director of the Collaborative Robotics and Intelligent Systems Institute, Oregon State University. Dr. Adams was the founder of the Human-Machine Teaming Laboratory at Vanderbilt University, prior to moving the laboratory to Oregon State. Adams has worked in the area of human-machine teaming for almost thirty years. Throughout her career she has focused on human interaction with unmanned systems, but also focused on manned civilian and military aircraft at Honeywell, Inc. and commercial, consumer and industrial systems at the Eastman Kodak Company. Her research, which is grounded in robotics applications for domains such as first response, archaeology, oceanography, the national airspace and the U.S. military, focuses on distributed artificial intelligence, swarms, robotics and human-machine teaming. Adams received her M.S. and Ph.D. degrees in Computer and Information Sciences from the University of Pennsylvania and her B.S. in Computer Science and B.B.E. in Accounting from Siena College.
Abstract: For autonomous robots to operate in the open, dynamically changing world, they will need to be able to learn a robust set of skills from relatively little experience. This talk begins by introducing Grounded Simulation Learning as a way to bridge the so-called reality gap between simulators and the real world in order to enable transfer learning from simulation to a real robot. It then introduces two new algorithms for imitation learning from observation that enable a robot to mimic demonstrated skills from state-only trajectories, without any knowledge of the actions selected by the demonstrator. Grounded Simulation Learning has led to the fastest known stable walk on a widely used humanoid robot, and imitation learning from observation opens the possibility of robots learning from the vast trove of videos available online.
Biography: I am the founder and director of the Learning Agents Research Group (LARG) within the Artificial Intelligence Laboratory in the Department of Computer Science at The University of Texas at Austin, as well as associate department chair and chair of the University's Robotics Portfolio Program. I am also the President, COO, and co-founder of Cogitai, Inc. My main research interest in AI is understanding how we can best create complete intelligent agents. I consider adaptation, interaction, and embodiment to be essential capabilities of such agents. Thus, my research focuses mainly on machine learning, multiagent systems, and robotics. To me, the most exciting research topics are those inspired by challenging real-world problems. I believe that complete successful research includes both precise, novel algorithms and fully implemented and rigorously evaluated applications. My application domains have included robot soccer, autonomous bidding agents, autonomous vehicles, autonomic computing, and social agents.
Abstract: Robust information exchange and trusted coordination are both critical needs for multi-robot systems acting in the real world. While these needs are universal across platforms, the computing and sensing resources of these platforms are not – making effective coordination difficult to enable, to scale, and to secure. This talk will present new methods of security and adaptive network formation for resource-constrained, mobile multi-robot systems (applications include delivery drones, mobile IoT, and robotic vehicles). The focus of this work is at the intersection of robotics and communication, and in particular, we study ways that communication technologies can be used to make resource-constrained multi-robot systems more capable. This talk will touch upon our developed technologies in 1) position control algorithms for multiple robots to achieve high data rate networks and 2) development of a virtual sensor for bi-directional Synthetic Aperture Radar between two communicating agents. Building upon these technologies, we develop a theoretical and experimental framework for provably thwarting spoofing attacks using communicated wireless signals in various important multi-agent tasks such as consensus, coverage, and drone delivery. This talk will have a particular focus on our most recent results in securing multi-agent consensus.
Biography: Stephanie is currently an Assistant Professor in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University (Jan 2018). Prior, she was a research scientist in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT where she also completed her Ph.D. work (2014) on multi-robot coordination and control and M.S. work (2009) on system identification and model learning. At MIT she collaborated extensively with the wireless communications group NetMIT, the result of which were two U.S. patents recently awarded in adaptive heterogeneous networks for multi-robot systems and accurate indoor positioning using Wi-Fi. She completed her B.S. at Cornell University in 2006.
Abstract: Robots are built to help humans improve the quality of life, from household tasks to intelligent transportation. This talk presents two important tasks that can be accomplished by the help of robots: collectively object manipulation and autonomous transportation. This talk first describes collective transport by multi agent and why this problem is difficult in general. An end-to-end fully distributed algorithm is presented to collectively retrieve a large object from an unknown GPS-denied environment a by a group of robots with limited sensing. This talk demonstrates all steps of collective transport from exploration and finding the object to planning the navigation path and manipulating the object among obstacles. The second part of the talk is to present a transferable model for accurate prediction pedestrian trajectories, as vulnerable road users, in crowded intersection corners or near crosswalks. Given prior knowledge of curbside geometry, the presented framework can accurately predict pedestrian trajectories even in new, unseen intersections. This is achieved by learning motion primitives in a common frame, called the curbside coordinate frame. Context features including pedestrian traffic light and distance to the curbside, enable us to build a transferable prediction model, which can be useful for individual autonomous cars or connected vehicles. The transferable model not only provides a framework to incrementally learn new motion behaviors, but the knowledge can also transfer and be shared between connected cars.
Biography: I‘m a postdoctoral researcher associate in aerospace control laboratory, MIT. I got my PhD in Computer science from Rice university. I have worked on broad aspects in robotics in AI, from multi agent systems, motion planning, to self-driving cars. During my PhD, I have designed distributed algorithms for multi agents and implemented on real robots. My PhD thesis focused on multi robot manipulation by a group of robots and multi robot recovery. Currently, I’m working on autonomous driving cars, specifically prediction the motion of pedestrian trajectories in crowded areas. I’m interested in designing a generalized transferable model to estimate the motion of vulnerable road users to improve the efficiency and safety of autonomous vehicles.