Organizational Unit:

Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link

https://hdl.handle.net/1853/70657

Includes Organization(s)

Organizational Unit

Biorobotics and Human Modeling Lab

Organizational Unit

Humanoid Robotics Laboratory

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Publication Search Results

Now showing 1 - 10 of 1280

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.
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.
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.
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.
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.
Robotics in the Era of Vision-Language Foundation Models

(Georgia Institute of Technology, 2023-11-29) Kira, Zsolt
Knowledge Driven Robotics: What the future holds

(Georgia Institute of Technology, 2023-11-29) Balakirsky, Stephen
Expectations and Needs for Interaction in Human Robot Interaction

(Georgia Institute of Technology, 2023-11-29) Feigh, Karen M.
Safe Humanoid Locomotion and Navigation: Challenges and Opportunities

(Georgia Institute of Technology, 2023-11-29) Zhao, Ye
Reliable Robotics at Scale

(Georgia Institute of Technology, 2023-11-29) Range, Brad ; Olivero, Daniel