Assistant Professor, Penn State
Title: Using Information Encoded in Motion to Close the loop in pHRI
Abstract: Robotics and haptics have the potential to enhance human performance and learning as well as provide unique insight into neuromotor function through sensing and quantification of human motion. At the same time, human behavior can inform the development of control strategies for complex tasks and human-robot interactions.
In physical human-robot interaction, information is communicated via motion — configurations, velocities, forces, and torques. The methods used for evaluation of motion greatly influences our ability to recognize the effects of assistance and training from a statistical standpoint, but more importantly, the mathematical structure imposed by unique measures of motion quality has significant impact on the algorithmic tools that are available to manage the interactions between robots and humans. This talk will focus on how motion measures affect the performance of the closed loop controller and our ability to statistically characterize differences in motion due to deficit, assistance, and learning.
Biography: Katie Fitzsimons is an Assistant Professor of Mechanical Engineering at the Pennsylvania State University. Prof. Fitzsimons earn her B.S. in Mechanical Engineering from Michigan State University in 2013, M.S. in Mechanical Engineering from Northwestern University in 2017, and Ph.D. from Northwestern in 2020. Dr. Fitzsimons' research interests lie at the interaction between humans and autonomous systems at both the level of an individual human-robot pair and the broader exchange between the fields of human motion analysis and robotic control. She was awarded the National Science Foundation Graduate Research Fellowship in 2014 and was awarded the National Defense Science and Engineering Graduate Fellowship in 2016.
National Taiwan University
Title: An introductory talk about the design of cam mechanisms
Abstract: Cam mechanisms have been widely implemented in modern applications, such as automatic tool changer (ATC), internal combustion engines, and pick-and-place machines. To improve the performance of these industrial applications using the cam mechanism, it is necessary to invent novel cam mechanisms that can overcome inherit disadvantages of commonly used types. Many of newly invented cam mechanisms with modified followers will be presented. Firstly, a disk cam mechanism with a translating follower that has symmetrical double rollers is found to have a better transmission angle on both the rising and the falling motions of the follower. Secondly, a positive-drive cam mechanism with dual concave faces of the follower is found to have lower contact stress over the constant-breadth and the constant-diameter cam mechanisms. At last, on the basis of an existing cam mechanism with a common roller follower, an extraneous intermediate link that has three rollers is added between the cam and the common follower. The contact forces and contact stresses of such cam mechanisms are analyzed to illustrate the advantage of spreading force transmission and reducing contact stress of this uncommon follower. In addition, the modified arrangement of the follower can be adapted to the stationary cam mechanism. Since the profiles of the stationary cam can be correspondingly determined according to desired path of the follower, the stationary cam mechanism can guide a follower with three rollers to move smoothly and free of backlash along a straight-curved stationary cam profile. Synthesis methodologies of these cam mechanisms will be shared during the Early Career Faculty Invited Presentation. Besides, it will be demonstrated that how these cam mechanisms could be a very economic and effective choice for the applications against a heavy loading or at a high speed.
Biography: Kuan-Lun Hsu is currently an assistant professor of Mechanical Engineering in National Taiwan University, Taipei, Taiwan. He holds a dual-degree at the doctoral level in Engineering at National Tsing Hua University and Tennessee Technological University. His research interests include kinematics and dynamics of machinery, mechanism and machine design, and cam-follower system.
Monroe Kennedy III
Title: Considerations for Human-Robot Collaboration Abstract: The field of robotics has evolved over the past few decades. We've seen robots progress from the automation of repetitive tasks in manufacturing to the autonomy of mobilizing in unstructured environments to the cooperation of swarm robots that are centralized or decentralized. These abilities have required advances in robotic hardware, modeling, and artificial intelligence. The next frontier is robots collaborating in complex tasks with human teammates, in environments traditionally configured for humans. While solutions to this challenge must utilize all of the advances of robotics, the human element adds a unique aspect that must be addressed. Collaborating with a human teammate means that the robot must have a contextual understanding of the task as well as all participant's roles. We will discuss what constitutes an effective teammate and how we can capture this behavior in a robotic collaborator. Biography: Monroe Kennedy III is an assistant professor in Mechanical Engineering at Stanford University. He leads the Assistive Robotics and Manipulation laboratory (arm.stanford.edu), which will develop robotic assistants by focusing on combining modeling and control techniques together with machine learning tools. Together, these techniques will improve performance for tasks that are highly dynamic, require dexterity, have considerable complexity, and require human-robot collaboration. Prof. Kennedy received his Ph.D. in Mechanical Engineering and Applied Mechanics and Masters in Robotics at the University of Pennsylvania, advised by Dr. Vijay Kumar, with a focus in robotics in the GRASP Lab. He was the recipient of GEM and NSF graduate fellowships. During his graduate studies, his research focused on increasing the abilities and effectiveness of robotic mobile manipulators performing complex service tasks in unstructured environments with considerations for working alongside human collaborators.
New Jersey Institute of Technology
Title: Measures of Motor Performance in Human and Robots
Abstract: The proposed talk will cover two main topics, reflecting my current and future research directions. Both topics pertain to the fundamental research question of quantifying aspects of motor performance in general mechanisms.
In the first part of the talk, I will discuss the theoretical and computational framework that is being developed for the characterization of the limits of dynamic balance in general biped systems. A systematic characterization of a threshold of balance in the state space of a given system is discussed. This threshold gives a prediction on the balancing capabilities of a given biped model, in terms of quantifiable ranges of feasible center of mass position and velocity. The characteristic shape of this balance threshold, called Boundary of Balance, can guide the design of legged mechanisms based on predefined balance requirements. This research is under experimental validation and will be translated into a technology for the quantitative assessment and training of balance performance in home-care settings.
In the second part of the talk, a model of motor task difficulty will be discussed, inspired by measures of complexity that are derived from information theory, such as Shannon’s entropy. The concept of difficulty is associated with the ability of the end-effector of a moving agent (robot or human) to accomplish a trajectory during a certain motor task. This ability depends on the fit between external (environment) and internal (agent) constraints, also known as affordance. A stochastic model of difficulty for a generalized reaching motor task is proposed as an affordance-related measure of perceived difficulty for an agent in a given task and environment. This research will prompt new perspectives in the study of movement affordance arising in the mutual interaction of humans, robots, and their environments.
The discussed research aims at providing novel benchmarks of motor behavior, which are essential for evaluating motor performance in robots, as well as in the robot-assisted workforce and in patients affected by impaired mobility.
In the long term, both topics will help steer the research paradigm on human and robot motion and control towards the new perspective of morphological intelligence, in which the complex morphology of mechanisms (e.g., large number of degrees-of-freedom, big dimension of actuation space, etc.) is not seen as an obstacle, but a source of embodied intelligence that can reduce the centralized computational demand at the higher level (e.g., brain or CPU).
Biography: Carlotta Mummolo is an Assistant Professor in the Department of Biomedical Engineering at the New Jersey Institute of Technology. Since September 2018, she is the director of the Coppélia Research Lab, where a diverse team of students and researchers studies principles of robot manipulation, locomotion, and balance stability, with applications in the field of motor rehabilitation. Dr. Mummolo received her bachelor and master degrees in Mechanical Engineering from Polytechnic of Bari (Italy) in 2009 and 2011, respectively, and a second master degree in Mechanical Engineering from New York University (NYU) in 2011. In January 2016, she received two doctoral degrees through a joint Ph.D. program in Mechanical Engineering between Polytechnic of Bari and NYU. She is member of ASME, IEEE Robotics and Automation Society, the International Society of Posture and Gait Research, and the NY Academy of Science. Her work is currently funded by NJIT Newark College of Engineering and by the European Union H2020 program.
The Hong Kong University of Science and Technology
Title: Design, Planning, and Control for Versatile Robotic Manipulation
Abstract: Robotic manipulation seeks to advance the way we handle objects of interest and interact with our environment using autonomous robotic systems. There are a wide range of applications such as transporting goods in warehouses, assembling parts in factories, and handling everyday objects to serve people directly, in which robots with advanced manipulation capabilities can be useful. In typical object manipulation scenarios, robots must be able to get a good hold on objects, transfer objects between secure grasps, and interact deliberately with the environment. Such tasks are still very difficult for robots today, whose versatility and dexterity are far below human levels. Achieving robotic dexterity and versatility in manipulation is thus one of the grand challenges in robotics. In this talk, I will introduce the research activities in my group on various robotic manipulation problems. Design, planning, and control techniques that will enable robots to skillfully manipulate objects and interact with the environment will be presented.
Biography: Jungwon Seo is an Assistant Professor of Mechanical and Aerospace / Electronic and Computer Engineering at The Hong Kong University of Science and Technology (HKUST), Clear Water Bay, Hong Kong. He is working towards advanced design, planning, and control for versatile robotic manipulation. He received a B.S. degree in Mechanical Engineering from Seoul National University, Seoul, Rep. of Korea. He earned his PhD from the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania, PA, USA. His honors include IEEE ICRA 2019 Best Paper Award in Robot Manipulation and IEEE ICRA 2014 Best Paper Award in Automation.
Adjunct Assistant Professor
Title: Leveraging Embedded Vision Sensors to Improve Robot's Performance
Abstract: Unlike conventional industrial robots that are kept separated from humans to ensure safety, the next generation robots physically interact with humans in a shared workspace. Soft robots are made of soft materials. They are inherently safe and ideal to be deployed in a human shared environment. However, their deformation is very complex which leads to great challenges for precise perception. I address this problem with embedded vision sensors thanks to their rich visual information. The high-resolution image data is capable of dealing with the complex deformation generated by the soft robots. In addition to the robot itself, I care about the robotic manipulation skills. Soft objects are exceedingly common in our daily life such as cables, clothes, towels, and fruits. But robotic manipulation of soft objects is very challenging because soft objects are deformable. They have infinite degrees of freedom, and their modeling and control are both very difficult. I address this problem with vision-based tactile sensors, which are able to capture the complex states (locally) of the soft objects. In this talk, I will present some of my recent works in these areas. First, I will discuss the development of an exoskeleton-covered soft robotic gripper that employs embedded vision sensors providing high-resolution proprioception and tactile sensing simultaneously. Second, I will present a vision-based tactile sensor that achieves high-resolution 3D reconstruction and is favorable for robotic manipulation. Finally, I will discuss the application of the vision-based tactile sensor for a robotic cable manipulation task.
Biography: Yu She will be joining Purdue University School of Industry Engineering as an assistant professor in 2021 Fall. He is currently an adjunct assistant professor at Purdue University and a postdoctoral researcher at MIT Computer Science & Artificial Intelligence Laboratory (CSAIL). He received his PhD in Mechanical Engineering at Ohio State University. He studies theoretical modeling, algorithm implementation, and prototype manufacturing for soft robots, human-safe collaborative robots, and tactile-reactive robotic hands. He received the presidential fellowship at the Ohio State University. He was a recipient of the best paper finalist at the 2020 Robotics: Science and Systems (RSS), the best paper award at the 2018 ASME Dynamic Systems & Control Conference (DSCC), and the best paper finalist at the 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO).
University of Pennsylvania
Title: Dynamical Robots via Origami-Inspired Design
Abstract: Origami-inspired engineering produces structures with high strength-to-weight ratios and simultaneously lower manufacturing complexity. This reliable, customizable, cheap fabrication and component assembly technology is ideal for robotics applications in remote, rapid deployment scenarios that require platforms to be quickly produced, reconfigured, and deployed. Unfortunately, most examples of folded robots are appropriate only for small-scale, low-load applications. In this talk, I will discuss efforts in my group to expand origami-inspired engineering to robots with the ability to withstand and exert large loads and to execute dynamic behaviors. I will show how the computational models of an origami design allow us to explore and optimize its mechanical response, and how we can leverage these designs for better performance and simpler control.
Biography: Cynthia Sung is the Gabel Family Term Assistant Professor in the Department of Mechanical Engineering and Applied Mechanics (MEAM) and a member of the General Robotics, Automation, Sensing & Perception (GRASP) lab at the University of Pennsylvania. She received a Ph.D. in Electrical Engineering and Computer Science from MIT in 2016 and a B.S. in Mechanical Engineering from Rice University in 2011. Her research interests are computational methods for design automation of robotic systems, with a particular focus on origami-inspired and compliant robots. She is the recipient of a 2019 NSF CAREER award, 2020 Johnson & Johnson Women in STEM2D Scholars Award, and a 2017 Popular Mechanics Breakthrough Award.
University of Alabama
Title: Mobility and Morphing of Modular Soft Robots
Abstract: Soft materials are deemed attractive for applications where adaptability and safe interaction with the environment are imperative. Imagine a swarm of terrestrial robots that can explore an environment, and upon completion of this task, reconfigure into a spherical ball and roll out. Realizing such versatile and robust systems poses design and mobility challenges. Morphological and topology design of such soft robot modules is based in age-old geometry concepts of platonic solids. While the control challenges are addressed by adopting an environment-centric perspective where graph theory is used to construct a probabilistic model of the environment. This generic, adaptable, robust and learning-oriented framework allows us to represent complex locomotion tasks as integer linear programming optimization problems which can be easily solved using commercial, off-the-shelf solvers.
Biography: Vishesh Vikas is an assistant professor in the Department of Mechanical Engineering, University of Alabama, Tuscaloosa (UA) and the director of the Agile Robotics Lab at UA (www.arl.ua.edu). His research interests are in the field of soft robotics, bioinspired robotics and tensegrity mechanisms. He received his MS (2008) and PhD (2011) in Mechanical Engineering from the Center of Intelligent Machines and Robotics (CIMAR), University of Florida, Gainesville. Thereafter, he was a postdoctoral researcher at the Neuromechanics and Biomimetics Devices, Tufts University (2012-2016). He has served as the Chair of Student Mechanisms and Robotics Design Competition at the ASME IDETC (2017-2019). Currently, he serves on the Electronic Editorial Board for the ASME Journal of Mechanisms and Robotics (JMR).
Yujiang (Mike) Xiang
Oklahoma State University
Title: Optimal Control of Multiple Powered Exoskeletons for Symmetric Human Lifting
Abstract: In this study, the optimal control of multiple powered exoskeletons for symmetric lifting motion is presented. The two-dimensional human model has 10 degrees of freedom. Four powered exoskeletons at knee, hip, spine, and elbow joints are used to aid the lifting motion. Physics-based motor dynamics are modeled with human mechanical system which is built by recursive Lagrangian dynamics. The gradient-based optimization is used to find the optimal lifting motion and controls of powered exoskeletons at multiple joints. In the optimization formulation, the design variables are the exoskeleton motor current profiles and human joint angle profiles. The inverse dynamics are used to calculate human and exoskeleton joint torques. The cost function is to minimize the sum of human joint torque squares for the lifting motion. The human and exoskeletons’ optimal joint torque profiles are reported. The optimal solution is obtained in several seconds of CPU time. The coupling of multiple powered exoskeletons at different joints for lifting is studied.
Biography: Dr. Yujiang (Mike) Xiang is an assistant professor in Mechanical and Aerospace Engineering Department at Oklahoma State University (OSU). Before he joined OSU, he was an assistant professor in Mechanical Engineering Department at University of Alaska Fairbanks. His research focuses on dynamic motion planning, rehabilitation mechanism design, exoskeleton optimal control, and related biomechanical modeling and evaluation.