Ross Knepper
1/25/17
The robotics seminar series will be kicked off this semester with a community discussion about the seminar and how it can best fulfill the needs of the community, i.e. build more connections among labs and departments, educate researchers about tools and techniques, and better inform interested parties about the latest and greatest research.
Will Thomason
2/8/17
The Robotic Personal Assistants Lab (RPAL) under PI Prof. Knepper investigates technologies to make robots behave as peers in collaborative tasks with people. In this seminar, several members of the lab will give informal chalk talks to describe their current research. These talks are meant to be interactive and accessible to a robotics audience. Rather than polished talks, these are snapshots of works in progress. We hope that this session will serve as a template for other labs at Cornell to emulate.
Chris Mavrogiannis
2/15/17
The Robotic Personal Assistants Lab (RPAL) under PI Prof. Knepper investigates technologies to make robots behave as peers in collaborative tasks with people. In this seminar, several members of the lab will give informal chalk talks to describe their current research. These talks are meant to be interactive and accessible to a robotics audience. Rather than polished talks, these are snapshots of works in progress. We hope that this session will serve as a template for other labs at Cornell to emulate.
2/22/17
obot swarms promise to offer solutions for applications that today are considered dangerous, expensive, or even impossible. Notable examples include construction, space exploration, mining, ocean restoration, nanomedicine, disaster response, and humanitarian demining. The diverse and large-scale nature of these applications requires the coordination of numerous robots, likely in the order of hundreds or thousands, with heterogeneous capabilities. Swarm engineering is an emerging research field that studies how to model, design, develop, and verify swarm systems. In this talk, I will discuss the aspects of swarm engineering that intersect with classical computer science. In particular, focusing on the concept of robot swarms as a “programmable machine”, I will analyze the issues that arise when one wants to write programs for swarms. After presenting Buzz, a programming language for swarms on which I worked during my postdoc, I will outline a number of open problems on which I intend to work over the next years.
Bio: Carlo Pinciroli is assistant professor at Worcester Polytechnic Institute, where he leads the NEST Lab. His research interests include swarm robotics and software engineering. Prof. Pinciroli obtained a Master’s degree in Computer Engineering at Politecnico di Milano, Italy and a Master’s degree in Computer Science at University of Illinois at Chicago, in 2005. He then worked for one year in several projects for Barclays Bank PLC group. In 2006 he joined the IRIDIA laboratory at Université Libre de Bruxelles in Belgium, under the supervision of Prof. Marco Dorigo. While at IRIDIA, he obtained a Diplôme d’études approfondies in 2007 and a PhD in applied sciences in 2014, and he completed a 8-month post-doctoral period. Between 2015 and 2016, Prof. Pinciroli was a postdoctoral researcher at MIST, École Polytechnique de Montréal in Canada under the supervision of Prof. Giovanni Beltrame. Prof. Pinciroli published 49 peer-reviewed articles and 2 book chapters, and edited 1 book. In 2015, F.R.S.-FNRS awarded him the most prestigious postdoctoral scholarship in Belgium (Chargé des Recherches).
Jim Jing and Scott Hamill
3/1/17
The Verifiable Robotics Research Group has been exploring different aspects of modularity in robot control and design. In this two part talk, Jim will describe current work on high-level control of modular robots (in collaboration with Mark Campbell’s and Mark Yim’s groups) and Scott will describe our initial thoughts on task-influenced design of modular soft robots (in collaboration with Rob Shepherd’s group).
Erik Komendera
3/8/17
Abstract: With the retirement of the Space Shuttle program, the option to lift heavy payloads to orbit has become severely constrained. Combined with the increasing success and decreasing costs of commercial small- to medium-lift launch vehicles, robotic in-space assembly is becoming attractive for mission concepts such as large space telescopes, assembly and repair facilities, solar electric propulsion tugs, and in situ resource utilization. Challenges in autonomous assembly include reasoning with uncertainties in the structure, agents, and environment, delegating a large variety of assembly tasks, and making error corrections and adjustments as needed. For space applications, the design and assembly of each part requires extensive planning, manufacturing, and checkout procedures. This hinders servicing, and prevents repurposing functional parts on derelict spacecraft. The advent of practical robotic in-space assembly will mitigate the need for deployment mechanisms and enable assembly using materials delivered by multiple launch vehicles. This reduction in complexity will lead to simplified common architectures, enabling interchangeable parts, and driving down costs
In recent years, Langley Research Center has developed assembly methods to address some of these challenges by distributing long reach manipulation tasks and precise positioning tasks between specialized agents, employing Simultaneous Localization and Mapping (SLAM) in the assembly workspace, using sequencing algorithms, and detecting and correcting errors. This talk will describe ongoing research, discuss the results of several recent robotic assembly experiments, and preview the upcoming assembly experiments to be performed under Langley’s “tipping point” partnership with Orbital/ATK.
Bio: Dr. Erik Komendera is a roboticist at NASA Langley Research Center in Hampton, VA. He earned his MS (’12) and PhD (’14) in Computer Science from the University of Colorado, and earned a BSE in Aerospace Engineering (’07) from the University of Michigan. Dr. Komendera’s current research focuses on autonomous assembly of structures in space, with a special focus on state estimation and machine learning techniques to identify and overcome errors in the assembly process. He currently serves as a task lead on the joint NASA/Orbital ATK Tipping Point project titled “Commercial Infrastructure for Robotic Assembly and Servicing” (CIRAS). In addition, he is Principal Investigator for a LaRC Center Innovation Fund / Internal Research and Development award to investigate machine learning methods for ensuring robust assembly and repair of solar array modules, and is a key member of the “Robotic Assembly of Modular Space Exploration Systems” research incubator effort.
Bennett Wineholt
3/22/17
Recent work to reduce the size and computational requirements of deep neural networks for machine learning has allowed applications including video object recognition and speech recognition to be performed responsively on small robotic systems which are commonly limited by power and payload constraints. This talk will present an application lifecycle for developing robot behaviors using deep learning techniques as well as describing advances in model compression which make these techniques more performant.
Bio: Bennett Wineholt is a staff member at the Cornell University Center for Advanced Computing supporting faculty needs for computing and consulting services to accelerate discovery.
Patrícia Alves-Oliveria
3/29/17
In this talk Patrícia will present her work on the field of Human-Robot Interaction. Specifically, she will introduce her previous work on the European project EMOTE whose goal was to develop a robotic tutor to support curricular activities in school. Additionally, Patrícia will present her initial work on creativity with robots.
Bio: Patrícia is a PhD student in psychology in an exchange program between Portugal and Cornell University. She is being supervised by Prof. Guy Hoffman and Prof. Ana Paiva (Gaips lab, Portugal) and she is studying how we can use robots to boot creativity in children.
Jesse Goldberg
4/12/17
Reinforcement learning enables animals to learn to select the most rewarding action in a given context. Edward Thorndike posed a simple solution to this problem in his Law of Effect: ‘Responses that produce a satisfying effect in a particular situation become more likely to occur again in that situation, and responses that produce a discomforting effect become less likely to occur again in that situation.’ This idea underlies stimulus-response, reinforcement, and instrumental learning and implementing it requires three pieces of information: (1) the action (response) an animal makes; (2) the context (situation) in which the action is taken; and (3) evaluation of the outcome (effect). In vertebrates, the basal ganglia have been proposed to integrate the three pieces of information required for reinforcement learning: (1) The situation, or current context, is thought to be signaled by a massive projection from the cortex to the striatum, the input layer of the BG; (2) The chosen action is signaled by striatal medium spiny neurons (MSNs) that drive behavior via projections to downstream motor centers; and (3) The evaluation of the outcome is transmitted to the striatum by midbrain DA neurons. These signals underlie a simple ‘three-factor learning rule’: If a cortical input is active (signifying a context), the MSN discharges (driving the action chosen), and an increase in DA subsequently occurs (signifying a good outcome), then the connection strength of the cortical input to the MSN is increased. Overall, by controlling the strength of the corticostriatal synapse, this dopamine-modulated corticostriatal plasticity governs which action will be chosen in a given context, placing DA in the premier position of determining what animals will learn and how they will behave. Here, I will discuss how our recent identification of dopaminergic error signals in birdsong support the potential generality dopamine modulated corticostriatal plasticity in implementing learning in a wide range of behaviors.
4/19/17
Abstract: Flapping flight is ubiquitous among agile natural flyers. Taking inspiration from biological flappers, we develop a robot capable of insect-like flight, and then go beyond biological capabilities by demonstrating multi-phase locomotion and impulsive water-air transition. In this talk, I will present our recent research on developing a hybrid aerial-aquatic microrobot and discuss the underlying physics. I will start by describing experimental and computational studies of flapping wing aerodynamics that aim to quantify fluid-wing interactions and ultimately distill scaling rules for robotic design. Comparative studies of fluid-wing interactions in air and water show remarkable similarities, which lead to the development of the first hybrid aerial-aquatic flapping wing robot. In addition to discussing the flapping frequency scaling rule and robot underwater stability, I will describe the challenges and benefits imposed by water surface tension. By developing an impulsive mechanism that utilizes electrochemical reaction, we further demonstrate robot water-air transition. I will conclude by outlining the challenges and opportunities in our current microrobotic research.
