IRIM Seminar Series

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Now showing 1 - 10 of 139
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    Regenstein Center for Bionic Medicine: Intuitive Control of Bionic Limbs
    (Georgia Institute of Technology, 2024-04-10) Hargrove, Levi
    Amputation is a leading cause of disability, and prosthetic devices are commonly accepted treatment options to restore functional capabilities. However, current prosthetic devices still cannot fully match the functionality of their natural counterparts. This talk focuses on the progress made in the development and control of bionic limbs for individuals with limb loss. The first portion of the talk provides an overview of the development, testing and commercialization of pattern recognition control systems for prosthetic arms, including their operation with advanced surgical techniques, such as targeted muscle reinnervation. A significant emphasis of this work has been on evaluation based on real user feedback, ensuring that the developed technologies meet the actual needs and preferences of end users. The second portion focuses on the application of these approaches (ie statistical pattern recognition and finite state-machines) to controlling powered leg prostheses. Finally, I will discuss our recent work in using deep-learning coupled with benchmark datasets (some collected at Georgia Tech) to remove the reliance of finite-state machines from our overall control approach.
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    A symbiotic philosophy for bio-inspired robotics
    (Georgia Institute of Technology, 2024-03-27) Moore, Talia
    Humans have frequently looked to natural phenomena to inspire the design of art, structures, and mechanisms. However, there are as many different ways to learn from nature as there are words for this approach: bioinspiration, biomimicry, and biodesign to name a few. In this talk, I propose a taxonomy for categorizing distinct biodesign approaches and use examples from my own research to illustrate the methodology and benefits of each. In particular, I introduce the field of Animal-Robot Interactions and describe how bio-inspired approaches can be used to further biological inquiry while advancing robotics.
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    From State Space Control to Intelligent Machines: A Five-Decade Journey in Mechanical Systems Control
    (Georgia Institute of Technology, 2024-03-06) Tomizuka, Masayoshi
    Masayoshi Tomizuka joined the Mechanical Engineering Department of UC Berkeley in 1974 after obtaining a PhD from MIT in the same year. It was an exciting time for someone in the field of dynamic systems and control. The 1960’s – 1970’s was the period when the state space control theories blossomed such as maximum principle, dynamic programming, Lyapunov stability, Kalman filtering, Linear Quadratic Gaussian Control and stability based adaptive control theory. At the same time, computer/information technology has made phenomenal advances during the period. At MIT Tomizuka used IBM1130 (with a card reader and printer) and a PDP-8 mini-computer. When he joined UC Berkeley, the campus mainframe computer was a CDC (Control Data Corporation) 6000 series computer, and the lab computer was PDP-7, which was upgraded to PDP-11, LSI-11, etc. The control program at Berkeley covered from both theory to implementation, and it was followed by many other schools. The1970’s was the time when a new generation of mechanical systems showed up; IBM introduced the Winchester Hard Disk Drive in 1973 and the FANUC Corporation was established in 1972. Tomizuka's laboratory, Mechanical Systems Control (MSC) Laboratory, naturally evolved to a group to study both mathematical and implementation aspects of controls. The current research emphasis of the MSC Laboratory is on intelligent industrial robots and autonomous driving. Several representative current projects will be introduced.
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    Control Principles for Robot Learning
    (Georgia Institute of Technology, 2024-02-07) Murphey, Todd
    Embodied learning systems rely on motion synthesis to enable efficient and flexible learning during continuous online deployment. Motion motivated by learning needs can be found throughout natural systems, yet there is surprisingly little known about synthesizing motion to support learning for robotic systems. Learning goals create a distinct set of control-oriented challenges, including how to choose measures as objectives, synthesize real-time control based on these objectives, impose physics-oriented constraints on learning, and produce analyses that guarantee performance and safety with limited knowledge. In this talk, I will discuss learning tasks that robots encounter, measures for information content of observations, and algorithms for generating action plans. Examples from biology and robotics will be used throughout the talk and I will conclude with future challenges.
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    Robotic Locomotion and Sensing on Deformable Terrains
    (Georgia Institute of Technology, 2024-01-24) Qian, Feifei
    Achieving robust mobility on natural and deformable terrains is pivotal for robots to operate effectively in real-world scenarios. Despite remarkable progress in robotics hardware and software, today’s robots still face challenges in traversing terrains like sand dunes, soft snow, and sticky mud, significantly trailing behind the locomotion abilities of animals and humans. This gap limits robots’ capabilities to aid in critical missions such as earthquake search and rescue, supply delivery, and planetary exploration. This talk discusses our recent efforts to bridge this gap. First, we show that by understanding the force responses from deformable terrains, we could allow robots to elicit desired ground reaction forces from challenging terrains like sand and mud and produce significantly improved locomotion performance. Second, we show that by leveraging the high force transparency of direct-drive actuators, robots could use their legs as proprioceptive sensors to determine substrate strength and mechanical properties. This proprioceptive sensing capability can enable robots to gather rich information from their environment during every step, and adapt their locomotion strategies accordingly. Finally, we discuss our latest progress in applying these locomotion and sensing strategies in earth and planetary exploration scenarios, and how the improved sensing and locomotion capabilities pave the way for new human-robot teaming workflows.
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    Do We Really Need all that Data? Learning and Control for Contact-rich Manipulation
    (Georgia Institute of Technology, 2023-11-15) Posa, Michael
    For all the promise of big-data machine learning, what will happen when robots deploy to our homes and workplaces and inevitably encounter new objects, new tasks, and new environments? If a solution to every problem cannot be pre-trained, then robots will need to adapt to this novelty. Can a robot, instead, spend a few seconds to a few minutes gathering information and then accomplish a complex task? Why does it seem that so much data is required, anyway? I will first argue that the hybrid or contact-driven aspects of manipulation clashes with the inductive biases inherent in standard learning methods, driving this current need for large data. I will then show how contact-inspired implicit learning, embedding convex optimization, can reshape the loss landscape and enable more accurate training, better generalization, and ultimately data efficiency. Finally, I will present our latest results on how these learned models can be deployed via real-time multi-contact MPC for robotic manipulation.
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    Dynamic Legged LocoManipulation: Balancing Reinforcement Learning with Model-Based Control
    ( 2023-04-12) Sreenath, Koushil
    Model-based control methods such as control Lyapunov and control barrier functions can provide formal guarantees of stability and safety for dynamic legged locomotion, given a precise model of the system. In contrast, learning-based approaches such as reinforcement learning have demonstrated remarkable robustness and adaptability to model uncertainty in achieving quadrupedal locomotion. However, reinforcement learning based policies lack formal guarantees, which is a known limitation. In this presentation, I will demonstrate that simple techniques from nonlinear control theory can be employed to establish formal stability guarantees for reinforcement learning policies. Moreover, I will illustrate the potential of reinforcement learning for more complex bipedal and humanoid robots, as well as for loco-manipulation tasks that entail both locomotion and manipulation. This brings up an intriguing question: Is reinforcement learning alone sufficient for achieving optimal results in dynamic legged locomotion, or is there still a need for model-based control methods?
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    Towards Human-Friendly Robots: We need more Robots at Home
    (Georgia Institute of Technology, 2023-03-08) Kim, Joohyung
    The demand for robots that can work in close proximity and interact physically with humans has been increasing. Currently, there are robots in airports, restaurants, and amusement parks that guide, serve, and entertain people. However, despite technological advancements, there are still very few robotic applications that meet the public’s expectations. To make robots more helpful in our daily lives, we need a better understanding of human environments and tasks, better methods for robots to perform tasks, and better designs for robots to interact with humans naturally and safely. In this presentation, I will share my experience and work on designing human-friendly robots through robot design, motion control, and human-robot interaction. Additionally, I will introduce KIMLAB’s recent approach to designing and implementing robots for home use.
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    Sort robots for humanity
    (Georgia Institute of Technology, 2023-03-01) Okamura, Allison M.
    Traditional robotic manipulators are constructed from rigid links and localized joints, which enables large forces and workspaces but creates challenges for safe and comfortable interaction with the human body. In contrast, many soft robots have a volumetric form factor and continuous bending that allows them to mechanically adapt to their environment — but these same mechanical properties can hinder forceful interactions required for physical assistance and feedback to humans. This talk will examine robotic systems and haptic devices that achieve the best of both worlds by leveraging softness and rigidity to enable novel shape control, generate significant interaction forces, and provide a compliant interface to the human body.
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    Making Large Dimensional Problems Small Again
    (Georgia Institute of Technology, 2023-02-22) Choset, Howie
    Motion is all around us. Motion is particularly interesting when it has many degrees of freedom. This talk covers the design, sensing, and planning for snake, multi-agent and modular robot high DOF systems. Thus far, each system requires different fundamentals – geometric mechanics for snake robot locomotion, deferred planning and ergodic search for multi-agent systems, and novel generator and discriminator networks for modular robots – which will be covered in this talk. While no grand unifying theory combines these approaches, they all share one aspect in common: reduce complex high dimensional problems into low dimensional ones. In pursuit of this investigation in reduction, my group has created several embedded systems - actuators and edge sensors - to build and deploy robots that stress-tests the core assumptions in the theory and demonstrates efficacy for applications of national importance. These applications include minimally invasive surgery, urban search and rescue, manufacturing, assembly in low-Earth orbit, maintenance of municipal infrastructure, and agile recycling. This talk will discuss these confined space applications, and if time permits, the five spin off companies, and one manufacturing institute, that my colleagues and I co-founded to commercialize the core technologies covered in this talk.