Tuesdays at 3:00-4:00pm, Upson 106 (Conference Room Next to the Lounge).
Light refreshments served starting at 2:45.
Fall 2018 Schedule
|8/28||Shuo Li, Cornell University||Additive Manufacturing of Soft Robots|
|This talk will present multidisciplinary work from material composites and robotics. We have created new types of actuators, sensors, displays, and additive manufacturing techniques for soft robots and haptic interfaces. For example, we now use stretchable optical waveguides as sensors for high accuracy, repeatability, and material compatibility with soft actuators. For displaying information, we have created stretchable, elastomeric light emitting displays as well as texture morphing skins for soft robots. We have created a new type of soft actuator based on molding of foams, new chemical routes for stereolithography printing of silicone and hydrogel elastomer based soft robots, and implemented deep learning in stretchable membranes for interpreting touch. All of these technologies depend on the iterative and complex feedback between material and mechanical design. I will describe this process, what is the present state of the art, and future opportunities for science in the space of additive manufacturing of elastomeric robots.|
|Wed 9/5||Adam Bry, Skydio||Scaling up autonomous flight|
NOTE: Special time and location: 5pm on Wednesday in Upson 106
Abstract: Drones hold enormous potential for consumer video, inspection, mapping, monitoring, and perhaps even delivery. They’re also natural candidates for autonomy and likely to be among the first widely-deployed systems that incorporate meaningful intelligence based on computer vision and robotics research. In this talk I’ll discuss the research we’re doing at Skydio, along with the challenges involved in building a robust robotics software system that needs to work at scale.
Bio: Adam Bry is co-founder and CEO of Skydio, a venture backed drone startup based in the bay area. Prior to Skydio he helped start Project Wing at Google[x] where he worked on the flight algorithms and software. He holds a SM in Aero/Astro from MIT and a BS in Mechanical Engineering from Olin College. Adam grew up flying radio controlled airplanes and is a former national champion in precision aerobatics. He was named to the MIT Tech Review 35 list in 2016.
9/4/18 Bonus Seminar this week!
4-5 p.m. in 203 Thurston Hall
Architectural Robotics: Ecosystems of Bits, Bytes and Biology
|9/11||Andy Ruina, Cornell University||Some Thoughts on Model Reduction for Robotics|
| These are unpublished thoughts, actually more questions than thoughts.And not all that well informed. So audience feedback is welcome. Especially from people who know about how to formulate machine learning problems
(I already know, sort of, how to formulate MatSheen learning problems).One posing of many robotics control problems is as a general problem in `motor control’ (a biological term, I think).Assume one has a machine and the best model (something one can compute simulations with) one can actually get of the machine, its environment, its sensors and its computation abilities. One also has some sense of the uncertainty in various aspects of these.The general motor problem is this: Given a history of sensor readings and requested goals (commands), and all of the givens above, what computation should be done to determine the motor commands so as to best achieve the goals.”Best” means, most accurately and most reliably by whatever measures one chooses.If one poses this as an optimization problem over the space of all controllers (all mappings from command and sensor histories to the set of commands), it is too big a problem, even if coarsely discretized.Hence, everyone applies all manner of assumed simplifications before attempting to make a controller.The question here is this, can one pose an optimization problem for the best simplification? Can one pose it in a way such that finding a useful approximate solution could be useful?In bipedal robots there are various classes of simplified models used by various people to attempt to control their robots. Might there be a rational way to choose between them, or find better ones?As abstract as this all sounds, perhaps thinking about such things could help us make better walking-robot controllers.
|9/18||Group Discussion||Big-data machine learning meets small-data robotics|
| Abstract: Machine learning techniques have transformed many fields, including computer vision and natural language processing, where plentiful data can be cheaply and easily collected and curated. Training data in robotics is expensive to collect and difficult to curate or annotate. Furthermore, robotics cannot be formulated as simply a prediction problem in the way that vision and NLP can often be. Robots must close the loop, meaning that we ask our learning techniques to consider the effect of possible decisions on future predictions. Despite exciting progress in some relatively controlled (toy) domains, we still lack good approaches to adapting modern machine learning techniques to the robotics problem. How can we overcome these hurdles? Please come prepared to discuss. Here are some potential discussion topics:
Please bring your own questions for the group to discuss, too!
|9/25||Cheng Zhang, Cornell University||Sensing + Interaction On and Around the Body|
Abstract: Wearables are a significant part of the new generation of computing. Compared with more traditional computers (e.g., laptop, smartphones), wearable devices are more readily available for immediate use, but significantly smaller in size, creating new opportunities and challenges for on-body sensing and interaction. My holistic research approach (from problem understanding to invention to implementation and evaluation) investigates how to effectively exchange information between humans, their environment, and wearables. My Ph.D. thesis focuses on novel wearable input using on-body sensing through various high-level interaction gestures, low-level input events, and a redesign of the interaction. In this talk, I will highlight three projects. The first is a wearable ring that allows the user to input over 40 unistroke gestures (including text and numbers). It also shows how to overcome a limited training set size, a common challenge in applying machine learning techniques to real systems, through an understanding of the characteristics of data and algorithms. The second project demonstrates how to combine a strong, yet incomplete, understanding of on-body signal propagation physics with machine learning to create a novel yet practical sensing and interaction techniques. The third project is an active acoustic sensing technique that enables a user to interact with wearable devices in the surrounding 3D space through continuous high-resolution tracking of finger’s absolute 3D position. It demonstrates how to solve a technical interaction challenge through a deep understanding of signal propagation. I will also share my vision on future opportunities for on-body sensing and interaction, especially in high-impact areas, such as health, activity recognition, AR/VR, and more futuristic interaction paradigms between humans and the increasingly connected environment.
Bio: Cheng Zhang is an assistant professor in Information Science at Cornell University. He received his Ph.D. in Computer Science at Georgia Institute of Technology, advised by Gregory Abowd (IC) and Omer Inan (ECE). His research focuses on enabling the seamless exchange of information among humans, computers, and the environment, with a particular emphasis on the interface between humans and wearable technology. His Ph.D. thesis presents 10 different novel input techniques for wearables, some leveraging commodity devices while others incorporate new hardware. His work blends an understanding of signal propagation on and around the body with, when necessary, appropriate machine learning techniques. His work has resulted in over a dozen publications in top-tier conferences and journals in the field of Human-Computer Interaction and Ubiquitous Computing (including two best paper awards), as well as over 6 pending U.S. and international patents. His work has attracted the attention of various media outlets, including ScienceDaily, DigitalTrends, ZDNet, New Scientist, RT, TechRadar, Phys.org<http://phys.org/>, Yahoo News, Business Insider, and MSN News. The work that leverages commodity devices has resulted in significant commercial impact. His work on novel interaction on smartwatch was licensed by Canadian startup ProximityHCI to improve the smartwatch interaction experience.
|10/2||Short Student Talks||Titles Below|
Presenter 1: Alap Kshirsagar, Hoffman Research Group
Title: Monetary-Incentive Competition between Humans and Robots: Experimental Results
Abstract: In this talk, I will describe an experiment studying monetary-incentive competition between a human and a robot. In this first of its kind experiment, participants (n=60) competed against an autonomous robot arm in ten competition rounds, carrying out a monotonous task for winning monetary rewards. For each participant, we manipulated the robot’s performance and the reward in each round. We found a small discouragement effect, with human effort decreasing with increased robot performance, significant at the p < 0.005 level. We also found a positive effect of the robot’s performance on its perceived competence, a negative effect on the participants’ liking of the robot, and a negative effect on the participants’ self-competence, all at p<0.0001.
Presenter 2: Carlos Araújo de Aguiar, Green Research Group
Title: transFORM – A Cyber-Physical Environment Increasing Social Interaction and Place Attachment in Underused, Public Spaces
Abstract: The emergence of social networks and apps has reduced the importance of physical space as a locus for social interaction. In response, we introduce transFORM, a cyber-physical environment installed in under-used, outdoor, public spaces. transFORM embodies our understanding of how a responsive, cyber-physical architecture can augment social relationship and increase place attachment. In this paper we critically examine the social interaction problem in the context of our increasingly digital society, present our ambition, and introduce our prototype which we will iteratively design and test. Cyber-physical interventions at large scale in public spaces are an inevitable future, and this paper serves to establish the fundamental terms of this frontier.
|10/9||Neil Dantam, Colorado School of Mines||Task and Motion Planning: Algorithms, Implementation, and Evaluation|
|Everyday tasks combine discrete and geometric decision-making. The robotics, AI, and formal methods communities have concurrently explored different planning approaches, producing techniques with different capabilities and trade-offs. We identify the combinatorial and geometric challenges of planning for everyday tasks, develop a hybrid planning algorithm, and implement an extensible planning framework. In ongoing work, we are improving the scalability and extensibility of our task-motion planner and developing planner-independent evaluation metrics.|
|10/16||Dylan Shell, Texas A&M University||Active Imperception and Naïve Robots|
|If robots become deeply interwoven in our lives, they’ll learn a great deal about us. Such robots may then disclose information about us, either if compromised by ne’er-do-wells or more simply when observed by third parties. In this talk I’ll describe a few ways we’ve been thinking about robotic privacy recently. This will include a privacy-preserving tracking problem, where we’ll look at how one might think about estimators which are constrained so as to ensure they never know too much. And also how we can solve planning problems subject to stipulations on the information divulged during plan execution. In these cases, sensors can provide too much information and an important question is: What sort of sensors are needed to ensure that the robot has the opportunity to cultivate ignorance. This is a robot design question—one which we’ll also examine briefly.|
|10/23||Short Student Talks||Titles Below|
Thais Campos de Almeida, Cornell University
Yuhan Hu, Cornell University
Haron Abdel-Raziq, Cornell University
|10/30||Short Student Talks||Titles Below|
Adam Pacheck, Cornell University
Yixiao Wang, Cornell University
Ryan O’Hern, Cornell University
|11/6||Short Student Talks||Titles Below|
Nialah Wilson, Cornell University
Wil Thomason, Cornell University
Ji Chen, Cornell University
|11/13||Tariq Iqbal, MIT|
|11/20||Tom Howard, University of Rochester|
|12/4 Short Student Talks Titles Below|
Matt Law, Cornell University
Steven Ceron, Cornell University
Chris Mavrogiannis, Cornell University