Previous Semesters

Wednesdays at 2pm in Upson 531.

Spring 2017 Schedule

1/25 Ross Knepper Robotics Community Discussion

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.

2/8 Wil Thomason Robotic Personal Assistants Lab Chalk Talks

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.

2/15 Chris Mavrogiannis Robotic Personal Assistants Lab Chalk Talks

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 Carlo Pinciroli Robot Swarms as a Programmable Machine

Robot 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).

3/1 Jim Jing and Scott Hamill Modularity and Design

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).

3/8  Erik Komendera  An Approach to Robotic In-Space Assembly

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.

3/15 Rob MacCurdy, MIT
3/22 Bennett Wineholt  Deep Learning for Hobby Robotics

 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.

3/29 Patrícia Alves-Oliveria Robots and Creativity

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.

4/12 Jesse Goldberg Dopamine based error signals suggest a reinforcement learning algorithm during song acquisition in birds

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 Kevin Chen Hybrid aerial-aquatic locomotion in an insect scale flapping wing robot
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.
4/26  Anil Rao A Computational Framework for Constrained Optimal Control Problems Using Gaussian Quadrature Collocation

Optimal control concerns systems that evolve in time for which you have partial control of the system and it is desired to optimize a specified performance criterion.   Optimal control problems arise in a variety of applications including engineering, economics, medicine, and epidemiology.

With a few notable exceptions (for example, the brachistochrone problem), virtually no optimal control problems have analytic solutions. Consequently, it is necessary to obtain a solution using numerical methods. Even with modern computers, solving optimal control problems numerically is a challenge because most optimal control problems of interest are nonlinear, high-dimensional, and have complex constraints.  As a result, finding accurate solutions to a general optimal control problem requires the development of sophisticated methods.

This seminar describes a framework for solving constrained optimal control problems.  The key approach described in this seminar is a class of variable-interval (h) variable-order (p) methods, also called hp-adaptive methods. In the hp-adaptive approach, a continuous optimal control problem is approximated as a finite-dimensional nonlinear optimization problem.  This class of hp-adaptive methods are employed using Gaussian quadrature to provide high-accuracy solutions using a significantly lower-dimensional discretization when compared with traditional fixed-order methods.

This seminar will first step through a motivation for the hp-adaptive approach. Recent research done in hp-adaptive mesh refinement techniques will be highlighted along with advances in methods for algorithmic differentiation.  The effectiveness of the approach will be demonstrated using the benchmark Bryson minimum time-to-climb of the F-4 supersonic aircraft.  Specifically, this aircraft flight example will demonstrate the significant improvements in computational efficiency gained by the hp-adaptive approach over previously developed methods.  Furthermore, a low-thrust Earth orbit transfer with eclipsing will be used to demonstrate the capability of the approach on a challenging space flight application.  Finally, future research directions will be discussed.


Anil V. Rao earned a BS in mechanical engineering and and AB in mathematics from Cornell, an MSE in aerospace engineering from the University of Michigan, and  an  MA and PhD from Princeton University. After earning his PhD, Dr. Rao joined the The Aerospace Corporation in Los Angeles abd was subsequently a Senior Member of the Technical Staff at The Charles Stark Draper Laboratory in Cambridge, Mass.  While at Draper, from 2001 to 2006, he was an adjunct faculty in the Department of Aerospace and Mechanical Engineering at Boston University,  where he taught the core undergraduate dynamics course.  Since 2006 he has been in Mechanical and Aerospace Engineering at the University of Florida where he is current an Associate Professor and Erich Farber Faculty Fellow. His research interests include computational methods for optimal control and trajectory optimization, nonlinear optimization, space flight mechanics, orbital mechanics, guidance, and navigation. He has co-authored the textbook Dynamics of Particles and Rigid Bodies: A Systematic Approach (Cambridge University Press, 2006)He is active in professional societies including the American Institute of Aeronautics and Astronautics, the American Astronautical Society, and the Society for Industrial and Applied Mathematics.  Dr. Rao serves on the editorial board of the Journal of the Astronautical Sciences, the Journal of Optimization Theory and Applications, and the Journal of Spacecraft and Rockets. He is the co-developer of the industrial-strength optimal control software GPOPS-II. His teaching and  research awards include the Department Teacher of the Year at BU (2002 and 2006) and at the University of Florida (2008), the College of Engineering Outstanding Teacher of the Year Award at BU (2004), the Book of the Year Award at Draper Laboratory (2006), the Pramod P. Khargonekar Junior Faculty Award (2012) at the University of Florida. He is an Associate Fellow of the American Institute of Aeronautics and Astronautics.


5/10  Thomas Wallin  Manufacturing techniques of soft robotics
 Conventional robots are composed of rigid components with discrete linkages that promote high precision and controllability; however, these systems require complex sensing and feedback controls and can struggle to perform in uncontrolled conditions.  Soft robots, by comparison, reduce the control complexity and manufacturing cost, while simultaneously allowing new, sophisticated functions.  While earlier generations of soft robots were limited architecturally and functionally, recent advances in materials and additive manufacturing technologies have enabled new and exciting capabilities.   In this talk, I will begin by discussing the essential elements of soft robots, highlighting the pertinent material properties.  Then I will describe the advantages and limitations of the different 3D printing technologies employed in both the indirect and direct fabrication of soft actuators.  For each manufacturing technique, we will discuss the compatible material classes with a focus on actuation and/or sensing mechanisms.

The schedule is maintained by Jessie White ( and Ross Knepper (

Fall 2016

8/24 Kirstin Petersen Designing Robot Collectives

In robot collectives, interactions between large numbers of individually simple robots lead to complex global behaviors. A great source of inspiration is social insects such as ants and bees, where thousands of individuals coordinate to handle advanced tasks like food supply and nest construction in a remarkably scalable and error tolerant manner. Likewise, robot swarms have the ability to address tasks beyond the reach of single robots, and promise more efficient parallel operation and greater robustness due to redundancy. Key challenges involve both control and physical implementation. In this seminar I will discuss an approach to such systems relying on embodied intelligent robots designed as an integral part of their environment, where passive mechanical features replace the need for complicated sensors and control.

The majority of my talk will focus on a team of robots for autonomous construction of user-specified three-dimensional structures developed during my thesis. Additionally, I will give a brief overview of my research on the Namibian mound-building termites that inspired the robots. Finally, I will talk about my recent research thrust, enabling stand-alone centimeter-scale soft robots to eventually be used in swarm robotics as well. My work advances the aim of collective robotic systems that achieve human-specified goals, using biologically-inspired principles for robustness and scalability.

8/31 Michael Duffy Boeing LIFT! Project – Cooperative Drones to Reduce the Cost of Vertical Flight

The LIFT! Project explored scaling of all-electric multi-rotor propulsion and methods of cooperation between multiple VTOL aircraft. Multi-rotor aircraft have become pervasive throughout the hobby industry, toy industry and research institutions due – in part – to very powerful, inexpensive inertial measurement devices and increased energy density of Li-Ion batteries driven by the mobile phone industry. This research demonstrates the viability of large multi-rotor systems up to two magnitudes of gross weight larger than a typical COTS hobby multi-rotor vehicle. Furthermore, this research demonstrates modularity and cooperation between large multi-rotor aircraft. In order to study large multi-rotor technologies, The Boeing Company decided to build a series of large scale multi-rotor vehicles ranging from 6 lbs gross weight to over 525 lbs gross weight using low cost COTS components. The LIFT! Project successfully demonstrated the effectiveness, modularity and scalability of electric multi-rotor technologies while identifying a useful load fraction (useful load/gross weight) of 0.64 for large, electric, unmanned multi-rotor aircraft. This research offers new insights on the feasibility of large electric VTOL aircraft, empirical trends, potential markets, and future research necessary for the commercial viability of electric VTOL aircraft.

9/7 Robotics Faculty Robotics Lab Hop

This week, the robotics research space on Upson Hall 5th floor opens its doors to the robotics seminar. See the newly-occupied labs of Profs. Ferrari, Shepherd, Kress-Gazit, Campbell, Ruina, and Knepper. Meet in the hallway outside of Upson 522.

9/14 Guy Hoffman Interacting with Robots through Touch: Materials as Affordances

Nonverbal behavior is at the core of human-robot interaction, but the subfield of social haptics is distinctly underrepresented. Most efforts focus around inserting sensors under a soft skin and using pattern recognition to infer a human’s tactile intention. There is virtually no work on robots touching humans in a social way, or robots responding to touch in a socially meaningful tactile manner. In that context, the advent of soft robotics and computational materials offers a new way for social robots to express internal and affective states. In the past, robot used mainly rotational and prismatic degrees of freedom for expression. How can new actuation technologies, such as shape-memory alloys, pneumatics, and “4D printed” structures contribute to new feedback methods and interaction paradigms? Also, how can we integrate traditional materials, such as wood, metals and ceramics to support the robot’s expressive capacity?

9/21 Keith Green Architectural Robotics: Ecosystems of Bits, Bytes, & Biology

Keith Evan Green looks toward a next frontier in robotics: interactive, partly intelligent, meticulously designed physical environments. Green calls this “Architectural robotics”: cyber-physical, built environments made interactive, intelligent, and adaptable by way of embedded robotics, or in William Mitchell’s words, “robots for living in.” In architectural robotics, computation—specifically robotics—is embedded in the very physical fabric of our everyday living environments at relatively large physical scales ranging from furniture to the metropolis. In this talk, Green examines how architectural robotic systems support and augment us at work, school, and home, as we roam, interconnect, and age.

9/28 Ross Knepper On the Communicative Aspect of Human-Robot Joint Action

Actions performed in the context of a joint activity comprise two aspects: functional and communicative.  The functional component achieves the goal of the action, whereas its communicative component, when present, expresses some information to the actor’s partners in the joint activity.  The interpretation of such communication requires leveraging information that is public to all participants, known as common ground. Humans cannot help but infer some meaning – whether or not it was intended by the actor – and so robots must be cognizant of how their actions will be interpreted in context.  In this talk, I address the questions of why and how robots can deliberately utilize this communicative channel on top of normal functional actions to work more effectively with human partners. We examine various human-robot interaction domains, including social navigation and collaborative assembly.

10/5 Ross Knepper Part II: On the Communicative Aspect of Human-Robot Joint Action

Part II — Actions performed in the context of a joint activity comprise two aspects: functional and communicative.  The functional component achieves the goal of the action, whereas its communicative component, when present, expresses some information to the actor’s partners in the joint activity.  The interpretation of such communication requires leveraging information that is public to all participants, known as common ground. Humans cannot help but infer some meaning – whether or not it was intended by the actor – and so robots must be cognizant of how their actions will be interpreted in context.  In this talk, I address the questions of why and how robots can deliberately utilize this communicative channel on top of normal functional actions to work more effectively with human partners. We examine various human-robot interaction domains, including social navigation and collaborative assembly.

10/12 Susan Herring Telepresence Robot Communication, Gender, and Metaphors of (Dis)ability

The principal use of telepresence robots is for human-human communication, where at least one person (the pilot) is remote via the robot and one or more persons (locals) are on site. It is important, therefore, to understand the nature of such communication – how locals perceive robot pilots as social actors, how robotic mediation affects interactional dynamics and norms, and how the experience of telepresence robot communication varies for different groups of users. In this talk, I address these questions through the dual lenses of gender and (dis)ability. I report the findings of a mock job interview study in which a male interviewer used a Beam+ telepresence robot, and the male and female interviewees were primed in advance with one of three metaphors about the interviewer – as a robot, as a (normal) human, or as a human with disabilities (cf. Takayama & Go, 2012). The interviews and reponses to a post-study survey were analyzed for interaction with, and attitudes toward, the robot interviewer. Initial results reveal differences across genders and across metaphorical priming conditions, but whereas the former are largely consistent with previous findings on gender and technology, the metaphor findings were unexpected. I discuss evidence that telepresence robot communication privileges some groups of communicators over others and suggest possible interventions – including metaphor manipulation and modifications to the robots themselves – to establish a level playing field before telepresence robot communication practices, which are currently emergent, become fixed.

Biographical Note: Susan Herring is Professor of Information Science and Linguistics at Indiana University, Bloomington. Mobility challenged herself, she uses and researches telepresence robots. She is also a long-time researcher of digitally-mediated communication, Director of IU’s Center for Computer-Mediated Communication, a past editor of the Journal of Computer-Mediated Communication, and current editor of Language@Internet.

10/19 Adrian Boteanu Verifiable Grounding and Execution of Natural Language Instructions

Robots are increasingly often expected to work along with humans. Natural language enables bi-directional interaction: for users to specify tasks and for the system to provide feedback. A significant challenge particular to this situated interaction is establishing correspondence between language and their physical meaning such as actions and objects, known as grounding. As both tasks and environments increase in complexity, the potential for ambiguity in interpreting the user’s statements increases.

I will present a grounding model which combines both physical and Linear Temporal Logic (LTL) representations to ground instructions. It allows for a formal specification to be generated from the grounding process. This specification is synthesized into a controller guaranteed to accomplish the task. Conversely, if synthesis is unsuccessful, it reveals problems such as logical inconsistencies in the specification or discrepancies between the specification and the physical environment.

In this latter case, the robot conveys these issues through natural language by referencing the physical environment and incorporates the user’s responses back into the specification. This robot-driven interaction enables the user to iteratively correct the grounded specification without requiring knowledge of the underlying representation.

11/30 Mike Meller Improving actuation efficiency through variable recruitment hydraulic McKibben muscles

Hydraulic control systems have become increasingly popular as the means of actuation for human-scale legged robots and assistive devices. One of the biggest limitations to these systems is their run time untethered from a power source. One way to increase endurance is by improving actuation efficiency. We investigate reducing servovalve throttling losses by using a selective recruitment artificial muscle bundle comprised of three motor units. Each motor unit is made up of a pair of hydraulic McKibben muscles connected to one servovalve. The pressure and recruitment state of the artificial muscle bundle can be adjusted to match the load in an efficient manner, much like the firing rate and total number of recruited motor units is adjusted in skeletal muscle. A volume-based effective initial braid angle is used in the model of each recruitment level. This semi-empirical model is utilized to predict the efficiency gains of the proposed variable recruitment actuation scheme versus a throttling-only approach. A real-time orderly recruitment controller with pressure-based thresholds is developed. This controller is used to experimentally validate the model-predicted efficiency gains of recruitment on a robot arm. The results show that utilizing variable recruitment allows for much higher efficiencies over a broader operating envelope.

12/7 Chris Mavrogiannis Decentralized Multi-Agent Navigation Planning with Braids

Navigating a human environment is a hard task for a robot, due to the lack of formal rules guiding traffic, the lack of explicit communication among agents and the unpredictability of human behavior. Despite the great progress in robotic navigation over the past few decades, robots still fail to navigate multi-agent human environments seamlessly. Most existing approaches focus on the problem of collision avoidance without explicitly modeling agents’ interactions. This often results in non-smooth robot behaviors that tend to confuse humans, who in turn react unpredictably to the robot motion and further complicate the robot’s decision making.

In this talk, I will present a novel planning framework that aims at reducing the emergence of such undesired oscillatory behaviors by leveraging the power of implicit communication through motion. Inspired by the collaborative nature of human navigation, our approach explicitly incorporates the concept of cooperation in the decision making stage, by reasoning over joint strategies of avoidance instead of treating others as separate moving obstacles. These joint strategies correspond to the spatiotemporal topologies of agents’ trajectories and are modeled using the topological formalism of braids. The braid representation is the basis for the design of an inference mechanism that associates agents’ past trajectories with future collective behaviors in a given context. This mechanism is used as a means of “social understanding” that allows agents to select actions that express compliance with the emerging joint strategy by compromising efficiency. Incorporating such a mechanism in the planning stage results in a rapid uncertainty decrease regarding the emerging joint strategy that facilitates all agents’ decision making. Simulated examples of multi-agent scenarios highlight the benefit of reasoning about joint strategies and appear promising for application in real-world environments.

Spring 2016

Date Speaker Title Host
2/3 Abhishek Anand Building machines worthy of being entrusted with human lives Ross Knepper
2/10 David Moroniti & Spyros Maniatopoulos Commercializing robotics: developing autonomous solutions for customer-driven problems Andy Ruina
2/17 Andy Ruina Recent thoughts on how to balance while walking Andy Ruina
2/24 Ross Knepper The Modern Prometheus: or Making Robots More Social May Harm HRI Ross Knepper
3/2 Ian Lenz Deep Learning for Robotics Ross Knepper
3/9 Cynthia Sung Roundtable discussion Wil Thomason
3/16 Kress-Gazit, Jung, Hoffman, Knepper, Ruina Research spotlights of several faculty Ross Knepper
3/23 Adrian Boteanu A Model for Verifiable Grounding and Execution of Complex Language Instructions Ross Knepper
4/13 Guy Hoffman Experimental Research in Progress on Robots and Ethics Ilse Van Meerbeek
4/20 Jesse Miller & Andy Ruina A directionally self-stable robotic sail boat: concept and simulations Ross Knepper
4/27 Huichan Zhao & Jonathan Jalving Optical Sensing and EMG Control of a Soft Orthosis Ilse Van Meerbeek
5/4 Spyros Maniatopoulos, Jennifer Padgett Practice ICRA Talks Ilse Van Meerbeek
5/11 Michael Meller Improving actuation efficiency through variable recruitment hydraulic McKibben muscles Ilse Van Meerbeek

Fall 2015

Date Speaker  Title Host
9/4 Thomas Wallin Hydrogel Stereolithography for Soft Robotics Ilse van Meerbeek
9/11 Steve Squyres Robotic Exploration of the Martian Surface with the Rovers Spirit and Opportunity Ross Knepper
9/18 Boris Kogan Bi-pedal robots: crude mechanical design methodologies Andy Ruina
9/25 Guy Hoffman Designing Robots for Fluent Collaboration Ross Knepper
10/2 Greg Stiesberg A passively stable hopping robot that isn’t passively stable Andy Ruina
10/9 Pingping Zhu Biophysical Modeling of Satisficing Control Strategies as Derived from Quantification of Primate Brain Activity and Psychophysics Silvia Ferrari
10/16 Malte Jung Robots and the Dynamics of Emotions in Work Teams Ross Knepper
10/23 Boris Kogan Slow little boats in a big fast ocean Andy Ruina
10/30 Spyros Maniatopoulos Reactive Robot Mission Planning: Bridging the Gap Between Theory and Practice
11/6 Minas Liarokapis (Yale) Adaptive Robot Hands: Challenges and Applications Ross Knepper
11/13 Matt Sheen Good Robot Simulation Andy Ruina
11/20 Mason Peck Control-Moment Gyroscopes for Low-Power Robotic Motion Ross Knepper
12/4 Hongchuan Wei Sensor Planning for Multiple Target Tracking Silvia Ferrari
12/11 Matt Kelly Trajectory Optimization – Overview and Tutorial Andy Ruina


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