April 2022 - Ai - Engineering Mailing Lists

Alignment reading group canceled today
by Pope, Quintin 29 Apr '22

29 Apr '22

Hello everyone, I'm again canceling the reading group for this week. Sorry about the inconsistent schedule. I hope to bring things back to more consistency within the next few weeks. All the best, Quintin Pope

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[CoRIS-List] OSU Robotics Seminar 4/29: ICRA 2022 practice talks
by Balasubramanian, Ravi 29 Apr '22

29 Apr '22

Folks: This week's OSU Robotics seminar will be presented by our own robotics students. The seminar will be an *in person* event and simulcast over Zoom. Friday April 29, 10—11 AM LINC 268 https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU… Here are the speakers and the topics: Helei Duan Time required: 4 min + questions Title: Sim-to-Real Learning of Footstep-Constrained Bipedal Dynamic Walking  Recently, work on reinforcement learning (RL) for bipedal robots has successfully learned controllers for a variety of dynamic gaits with robust sim-to-real demonstrations. In order to maintain balance, the learned controllers have full freedom of where to place the feet, resulting in highly robust gaits. In the real world however, the environment will often impose constraints on the feasible footstep locations, typically identified by perception systems. Unfortunately, most demonstrated RL controllers on bipedal robots do not allow for specifying and responding to such constraints. This missing control interface greatly limits the real-world application of current RL controllers. In this paper, we aim to maintain the robust and dynamic nature of learned gaits while also respecting footstep constraints imposed externally. We develop an RL formulation for training dynamic gait controllers that can respond to specified touchdown locations. We then successfully demonstrate simulation and sim-to-real performance on the bipedal robot Cassie. In addition, we use supervised learning to induce a transition model for accurately predicting the next touchdown locations that the controller can achieve given the robot's proprioceptive observations. This model paves the way for integrating the learned controller into a full-order robot locomotion planner that robustly satisfies both balance and environmental constraints.  Alexander You Time required: 4 min + questions Precision fruit tree pruning using a learned hybrid vision/interaction controller Robotic tree pruning requires highly precise manipulator control in order to accurately align a cutting implement with the desired pruning point at the correct angle. Simultaneously, the robot must avoid applying excessive force to rigid parts of the environment such as trees, support posts, and wires. In this paper, we propose a hybrid control system that uses a learned vision-based controller to initially align the cutter with the desired pruning point, taking in images of the environment and outputting control actions. This controller is trained entirely in simulation, but transfers easily to real trees via a neural network which transforms raw images into a simplified, segmented representation. Once contact is established, the system hands over control to an interaction controller that guides the cutter pivot point to the branch while minimizing interaction forces. With this simple, yet novel, approach we demonstrate an improvement of over 30 percentage points in accuracy over a baseline controller that uses camera depth data. Capprin Bass Time required: 4min + questions Title: Characterizing Error in Noncommutative Geometric Gait Analysis Authors: Capprin Bass and Ross L. Hatton Abstract: A key problem in robotic locomotion is in finding optimal shape changes to effectively displace systems through the world. Variational techniques for gait optimization require estimates of body displacement per gait cycle; however, these estimates introduce error due to unincluded high order terms. In this paper, we formulate existing estimates for displacement, and describe the contribution of low order terms to these estimates. We additionally describe the magnitude of higher (third) order effects, and identify that choice of body coordinate, gait diameter, and starting phase influence these effects. We demonstrate that variation of such parameters on two example systems (the differential drive car and Purcell swimmer) effectively manages third order contributions. Jeremy Dao Time required: 4 min + questions Title: Sim-to-Real Learning for Bipedal Locomotion Under Unsensed Dynamic Loads Authors: Jeremy Dao, Kevin Green, Helei Duan, Alan Fern, Jonathan Hurst Abstract: Recent work on sim-to-real learning for bipedal locomotion has demonstrated new levels of robustness and agility over a variety of terrains. However, that work, and most prior bipedal locomotion work, have not considered locomotion under a variety of external loads that can significantly influence the overall system dynamics. In many applications, robots will need to maintain robust locomotion under a wide range of potential dynamic loads, such as pulling a cart or carrying a large container of sloshing liquid, ideally without requiring additional load-sensing capabilities. In this work, we explore the capabilities of reinforcement learning (RL) and sim-to-real transfer for bipedal locomotion under dynamic loads using only proprioceptive feedback.  We show that prior RL policies trained for unloaded locomotion fail for some loads and that simply training in the context of loads is enough to result in successful and improved policies.  We also compare training specialized policies for each load versus a single policy for all considered loads and analyze how the resulting gaits change to accommodate different loads. Finally, we demonstrate sim-to-real transfer, which is successful but shows a wider sim-to-real gap than prior unloaded work, which points to interesting future research.  Tim Player Time required: 10 min + questions Title: Closed-Loop Generative Grasping with Temperospatial Sparse Convolution Authors: Tim Player, Dongsik Chang, Fuxin Li, Geoffrey Hollinger Abstract: Robotic grasp synthesis on unseen objects is a crucial skill that enables the application of generalized robotics to manufacturing, home service, and scientific applications. While state-of-the-art robotic grasping systems achieve reliable performance grasping previously unseen objects in laboratory conditions, robots deployed in real-world environments must continue to operate when visual conditions are degraded or when environments are dynamic. Motivated by the problem of underwater sample collection, we propose a novel algorithm that identifies 6-DOF grasp poses in a streaming depth video in real time, enabling operation in dynamic environments through closed loop control. Our technique uses temperospatial sparse convolution to process information from consecutive depth images in parallel, improving robustness to temporary occlusions. Our preliminary results indicate that the proposed grasp synthesis method is suitable for closed loop grasping. ----- Ravi Balasubramanian (he/him/his) Associate Professor Collaborative Robotics and Intelligent Systems (CoRIS) Institute School of Mechanical, Industrial, and Manufacturing Engineering Oregon State University Corvallis, OR 97331. ravi.balasubramanian(a)oregonstate.edu<mailto:ravi.balasubramanian@oregonstate.edu> <mailto:ravi.balasubramanian@oregonstate.edu><mailto:ravi.balasubramanian@oregonstate.edu> Graf Hall 315 Ph#: 541-737-4267 http://web.engr.oregonstate.edu/~balasubr/ Zoom: https://oregonstate.zoom.us/j/7036813309 Meeting password: robotics OSU Robotics: http://coris.oregonstate.edu/

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AI seminar: May 4, 2022
by Mangannavar, Rajesh Devaraddi 28 Apr '22

28 Apr '22

Dear all, Our next AI seminar on *"Probabilistic Planning through the Lens of Approximate Inference" *by Roni Khardon is scheduled to be on May 4th, 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Probabilistic Planning through the Lens of Approximate Inference* Roni Khardon Professor Department of Computer Science Indiana University, Bloomington *Abstract:* Planning to control an agent in a probabilistic environment requires reasoning about actions, their outcomes and their effects on the overall utility of the agent. The talk will emphasize the role of approximate probabilistic inference as a tool for understanding and developing new planning algorithms. I will first discuss planning through a differentiable symbolic form of belief propagation, and our corresponding system SOGBOFA that yields state of the art performance in a range of problems with large combinatorial action spaces. I will then discuss a more general framework that combines inference style and approximation style to derive old and new planning algorithms and show some preliminary results on their empirical performance. Time permitting I will show how these ideas can be extended for control in continuous spaces, with applications for reinforcement learning and robotics. *Speaker Bio:* Roni Khardon is a professor in the Department of Computer Science at Indiana University, Bloomington. He holds a Ph.D. in Computer Science from Harvard University, and M.Sc. and B.Sc. degrees from the Technion. Prior to moving to Bloomington he held faculty positions at the University of Edinburgh (1997-2000) and at Tufts University (2000-2018). His research interests are in developing agents that can learn from data, build representations of their world, use such knowledge for reasoning and decision making, and act in their environment so as to optimize their objectives. His recent work spans topics in AI (probabilistic planning, knowledge representation), machine learning (graphical models, approximate inference, computational learning theory) and the connections between these areas. This talk will be followed by a talk on the topic "*Graphical Models for Handling Data that may be Incomplete or Non-iid*" by professor Karthika Mohan and Chi Zhang on 11th May (Wednesday) 1-2 PM, 2022 *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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(fwd) Internship and Full time positions at Oracle
by Weng-Keen Wong 27 Apr '22

27 Apr '22

My contact at Oracle sent this ad to me for internship and full-time opportunities. Please respond directly to Oracle if interested. Weng-Keen --------------- Job Title: Data Scientist Short description Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights, Machine Learning Models and solutions for CX Marketing products. Job Description Our future success depends on hiring outstanding, early in career talent who are looking to power next-generation services and solutions. We are hiring bachelor's, master's, and PhD graduates with a diverse set of skills and experiences to work in and across multiple teams within Oracle. Our program is intended to enhance your overall learning experience, help build your network, and accelerate your chance to make an impact. Create the future with us! The Company: Oracle is the world's leading provider of business software. With a presence in over 175 countries, we are one of the biggest technology companies in the world. We're using innovative emerging technologies to tackle real-world problems today. From advancing energy efficiency to reimagining online commerce, the work we do is not only redefining the world of business-it's helping advance governments, power nonprofits, and giving billions of people the tools they need to outpace change. For more information about Oracle (NYSE:ORCL), visit us at oracle.com. What You'll Do: Data Scientist responsibilities may include, but are not limited to: * Work on multiple stages of data science projects, including data pipeline, aggregation, feature engineering, modeling and visualization. * Develop models to build solutions to tackle real-world business problems * Interact with product and service teams to identify questions and issues for data analysis and experiments * Communicate your analysis findings to product, service, and business managers We are seeking to grow the data science capabilities with forward-thinking and diverse individuals with exceptional technical ability. This is an exciting role that will stretch your knowledge and curiosity, while at the same time is an excellent opportunity to learn new skills and work within an unusually talented, global community at Oracle. What You'll Bring (Objective Minimum Qualifications): To be considered for a Data Scientist position, you must possess the Objective Minimum Qualifications (OMQs) below. Please ensure that your application clearly indicates that you meet these OMQs, either on your resume and/or by uploading additional documents such as a transcript. * Have at least a Bachelor's degree in an applied business or quantitative field such as Statistics, Operations Research, Applied Mathematics, Computer Science, Economics, Business Analytics, or equivalent field by August 2022. * Have no more than 12 months of professional full-time work experience in the technology field (excluding internships, research and/or teaching assistant roles, and military experience). * Have academic coursework, projects, internships, and/or research experience with tools needed to execute the more technical aspects of statistical data analysis, including proficiency (e.g., can complete projects without any assistance) in two or more of the following programming languages: o Python, Java, R, SQL, PL/SQL. * Have experience through coursework, projects, internships, and/or research with one or more of the following libraries: o Numpy, Scipy, Matplotlib, SciKit, TensorFlow, PyTorch, Spark * Have completed coursework, projects, internships, and/or research in three or more of the following: o Algorithms o Artificial Intelligence o Big Data Query o Data Mining o Data Modeling o Machine Learning o Statistical Inference/Analysis * Have completed at least one internship, research assistantship, teaching assistantship, or equivalent practical experience in one of the Data Science areas listed above. * Reside in the United States and/or attend a university in the US. * Authorized to work in the US in 2022. Life at Oracle: An Oracle career can span industries, roles, countries and cultures, giving you the opportunity to tackle new roles and challenges, while blending work and life. Oracle has thrived through 40+ years of change by innovating and operating with integrity while delivering for the top companies in almost every industry. To nurture the talent that makes this happen, we work hard to build a vibrant and inspiring workplace that celebrates diverse, hardworking teams where everyone can contribute. We take care of each other, and value giving back to the community. We have flexible work arrangements and offer benefits including generous paid parental leave and comprehensive premium medical insurance. Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse "big data" sources to generate actionable insights and solutions for client services and product enhancement. Interacts with product and service teams to identify questions and issues for data analysis and experiments. Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources. Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers. Duties and tasks are standard with some variation. Completes own role largely independently within defined policies and procedures. 2 years relevant experience and BA/BS degree preferred. If you are a Colorado resident, Please Contact us or Email us at oracle-salary-inquiries_us(a)oracle.com <mailto:oracle-salary-inquiries_us@oracle.com> to receive compensation and benefits information for this role. Please include this Job ID: 126881 in the subject line of the email.

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[CoRIS-List] Updated Schedule: Oregon State Robotics Spring 2022 Seminar Series
by Ross L. Hatton 26 Apr '22

26 Apr '22

Hi All, I am very pleased to announce that the OSU Robotics seminar on May 20 will be presented by Reid Simmons of the Carnegie Mellon University Robotics Institute. (Chris Sanchez from OSU Psychology, has graciously agreed to shift his OSU Robotics seminar to next year.) The full updated schedule for the current term is below. --- 4/1/2022 Cristina Wilson -UPenn GRASP Lab & RAD Lab -Collaborative human-robot teaming -https://www.radlab.us 4/8/2022 RGSA Round Table 4/15/2022 Chris Neider -Starship Technologies -Food delivery robots -https://www.starship.xyz/ 4/22/2022 Kim Ingraham -University of Washington Mechanical Engineering, Rehabilitation Medicine -Biomechanics and wearable robots -https://kim-ingraham.com 4/29/2022 ICRA practice talks 5/6/2022 Shai Revzen -University of Michigan Electrical Engineering -Dynamics, bioinspired, legged locomotion -https://robotics.umich.edu/profile/shai-revzen/ 5/13/2022 No Seminar: NW Robotics Symposium at University of Washington 5/20/2022 Reid Simmons -Carnegie Mellon University Robotics Institute -Robust autonomy -Human-Robot interaction 5/27/2022 No seminar: ICRA 6/3/2022 Edward Wang -UCSD ECE & Design Lab -Design and health monitoring -https://www.ejaywang.com (Kristen Macuga from OSU Psychology was originally scheduled to speak this term, but has rescheduled for the Fall) =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu research.engr.oregonstate.edu/lram/ rosslhatton.com ross.hatton(a)oregonstate.edu

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AI seminar: April 27, 2022
by Mangannavar, Rajesh Devaraddi 26 Apr '22

26 Apr '22

Dear all, Our next AI seminar on *"Building neural networks that know what they don’t know" *by Balaji Lakshminarayanan is scheduled to be on April 27th(Tomorrow), 1-2 PM PST (Add to google calendar <https://calendar.google.com/event?action=TEMPLATE&tmeid=YXExYTZuY2NiZXRhbHJ…> ). It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Building neural networks that know what they don’t know* Balaji Lakshminarayanan Staff Research Scientist Google Brain *Abstract:* Deep neural networks can make overconfident errors and assign high confidence predictions to inputs far away from the training data. Well-calibrated predictive uncertainty estimates are important to know when to trust a model's predictions, especially for safe deployment of models in applications where the train and test distributions can be different. I'll first present some concrete examples that motivate the need for uncertainty and out-of-distribution (OOD) robustness in deep learning. Next, I'll present an overview of our recent work focused on building neural networks that know what they don’t know: this includes methods which improve single model uncertainty (e.g. spectral-normalized neural Gaussian processes), methods which average over multiple neural network predictions such as Bayesian neural nets and deep ensembles, and methods that leverage better representations (e.g. improving “near-OOD” detection). *Speaker Bio:* Balaji Lakshminarayanan is a staff research scientist at Google Brain. His recent research is focused on probabilistic deep learning, specifically, uncertainty estimation, out-of-distribution robustness and applications. Before joining Google Brain, he was a research scientist at DeepMind. He received his Ph.D. from the Gatsby Unit, University College London and Master’s degree from Oregon State University. He has co-organized several workshops on "Uncertainty and Robustness in deep learning" and served as Area Chair for NeurIPS, ICML, ICLR, and AISTATS. This talk will be followed by a talk on the topic "*Probabilistic Planning through the Lens of Approximate Inference*" by professor Roni Khardon on 4th May (Wednesday) 1-2 PM, 2022 *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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[CoRIS-List] Reminder: OSU Robotics Seminar Today: Kim Ingraham, "Enabling human mobility through personalized robotic control"
by Ross L. Hatton 22 Apr '22

22 Apr '22

Hi All, As a reminder, this week's OSU Robotics seminar will be presented by Kim Ingraham from the University of Washington, on "Enabling human mobility through personalized robotic control". The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. The seminar will be an *in person* event, simulcast over Zoom, and will be immediately followed by a half-hour students-only Q&A session with the speaker. April 22, 10am LINC 268 https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU… <https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU…> ----------------- Seminar Title: Enabling human mobility through personalized robotic control Abstract: Assistive robotic technologies—like protheses, exoskeletons, and semi-autonomous powered wheelchairs—have the potential to meaningfully transform human mobility. Yet, despite great strides in the development of wearable assistive robots over the last several decades, people are not widely using these systems in their daily lives. Fundamentally, we do not yet know how to apply robotic assistance to the human body in order to promote meaningful clinical improvements or achieve targeted physiological goals. To this end, my work is focused on the design and experimental evaluation of personalized, adaptive control strategies for assistive robotic devices. In this seminar, I will present work that advances our understanding of how to provide robotic assistance to users outside the laboratory environment. As an example of a personalized control system, I will demonstrate that exoskeleton users can quickly and precisely identify features of robotic assistance that they prefer, and highlight characteristics of user preference that make the design of personalized control systems both compelling and challenging. While many assistive robots support users during walking, I will also share insights into how these methods may be applied to wheeled mobility technologies, specifically for children with disabilities. Together, this work supports my future research goal of designing personalized, adaptive control strategies for wearable assistive robots in order to enable people to meet their goals and achieve full participation in their daily lives. Bio: Kim Ingraham (she/her) is a post-doctoral fellow at the University of Washington in the departments of Rehabilitation Medicine and Mechanical Engineering, and is a member of the multidisciplinary group CREATE (Center for Research and Education on Accessible Technology and Experiences). Kim holds degrees in Mechanical Engineering from the University of Michigan (PhD, 2021) and in Biomedical Engineering from Vanderbilt University (BE, 2012). Prior to beginning graduate school, she worked on an interdisciplinary team of scientists, engineers, and clinicians as a Research Engineer at the Shirley Ryan AbilityLab (2012-2015). Kim is passionate about using assistive wearable robots to advance human mobility in all forms. In her research, she has developed and evaluated physiologically-inspired control systems for a variety of assistive devices, including powered lower-limb prostheses, robotic exoskeletons, and powered wheelchairs for young children with disabilities. Kim is an NSF graduate research fellow, and served as both a graduate student instructor and an engineering teaching consultant at the University of Michigan. =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu <http://coris.oregonstate.edu/> research.engr.oregonstate.edu/lram/ rosslhatton.com ross.hatton(a)oregonstate.edu

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AI seminar: April 27, 2022
by Mangannavar, Rajesh Devaraddi 21 Apr '22

21 Apr '22

Dear all, Our next AI seminar on *"Building neural networks that know what they don’t know" *by Balaji Lakshminarayanan is scheduled to be on April 27th, 1-2 PM PST. It will be followed by a 30-minute Q&A session by the graduate students. Zoom Link: https://oregonstate.zoom.us/j/93591935144?pwd=YjZaSjBYS0NmNUtjQzBEdzhPeDZ5U… *Building neural networks that know what they don’t know* Balaji Lakshminarayanan Staff Research Scientist Google Brain *Abstract:* Deep neural networks can make overconfident errors and assign high confidence predictions to inputs far away from the training data. Well-calibrated predictive uncertainty estimates are important to know when to trust a model's predictions, especially for safe deployment of models in applications where the train and test distributions can be different. I'll first present some concrete examples that motivate the need for uncertainty and out-of-distribution (OOD) robustness in deep learning. Next, I'll present an overview of our recent work focused on building neural networks that know what they don’t know: this includes methods which improve single model uncertainty (e.g. spectral-normalized neural Gaussian processes), methods which average over multiple neural network predictions such as Bayesian neural nets and deep ensembles, and methods that leverage better representations (e.g. improving “near-OOD” detection). *Speaker Bio:* Balaji Lakshminarayanan is a staff research scientist at Google Brain. His recent research is focused on probabilistic deep learning, specifically, uncertainty estimation, out-of-distribution robustness and applications. Before joining Google Brain, he was a research scientist at DeepMind. He received his PhD from the Gatsby Unit, University College London and Master’s degree from Oregon State University. He has co-organized several workshops on "Uncertainty and Robustness in deep learning" and served as Area Chair for NeurIPS, ICML, ICLR and AISTATS. *Please watch this space for future AI Seminars :* * https://eecs.oregonstate.edu/ai-events <https://eecs.oregonstate.edu/ai-events>* Rajesh Mangannavar, Graduate Student Oregon State University ---- AI Seminar Important Reminders: -> For graduate students in the AI program, attendance is strongly encouraged.

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AI Alignment Reading Group
by Pope, Quintin 21 Apr '22

21 Apr '22

Hello everyone, We’ll be holding the next AI alignment reading group meeting at 2 PM PST this Friday. Our paper will be “Active Learning Helps Pretrained Models Learn the Intended Task <https://arxiv.org/abs/2204.08491>”. Models can fail in unpredictable ways during deployment due to task > ambiguity, when multiple behaviors are consistent with the provided > training data. An example is an object classifier trained on red squares > and blue circles: when encountering blue squares, the intended behavior is > undefined. We investigate whether pretrained models are better active > learners, capable of disambiguating between the possible tasks a user may > be trying to specify. Intriguingly, we find that better active learning is > an emergent property of the pretraining process: pretrained models require > up to 5 times fewer labels when using uncertainty-based active learning, > while non-pretrained models see no or even negative benefit. We find these > gains come from an ability to select examples with attributes that > disambiguate the intended behavior, such as rare product categories or > atypical backgrounds. These attributes are far more linearly separable in > pretrained model's representation spaces vs non-pretrained models, > suggesting a possible mechanism for this behavior. > If you're at all interested in building AI systems that can disambiguate concepts or handle uncertainty, please feel free to join. All the best, Quintin Pope Join Zoom Meeting https://oregonstate.zoom.us/j/95843260079?pwd=TzZTN0xPaFZrazRGTElud0J1cnJLU… Password: 961594 Phone Dial-In Information +1 971 247 1195 US (Portland) +1 253 215 8782 US (Tacoma) +1 301 715 8592 US (Washington DC) Meeting ID: 958 4326 0079 All the best, Quintin

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[CoRIS-List] OSU Robotics Seminar 4/22: Kim Ingraham, "Enabling human mobility through personalized robotic control"
by Ross L. Hatton 19 Apr '22

19 Apr '22

Hi All, This week's OSU Robotics seminar will be presented by Kim Ingraham from the University of Washington, on "Enabling human mobility through personalized robotic control". The seminar will be from 10-11am, followed by an 11-11:30 student-only Q&A session. The seminar will be an *in person* event, simulcast over Zoom, and will be immediately followed by a half-hour students-only Q&A session with the speaker. April 22, 10am LINC 268 https://oregonstate.zoom.us/j/93908195212?pwd=cEdoQVdac0JxMUpOSS9xVzZjVG5xU… ----------------- Seminar Title: Enabling human mobility through personalized robotic control Abstract: Assistive robotic technologies—like protheses, exoskeletons, and semi-autonomous powered wheelchairs—have the potential to meaningfully transform human mobility. Yet, despite great strides in the development of wearable assistive robots over the last several decades, people are not widely using these systems in their daily lives. Fundamentally, we do not yet know how to apply robotic assistance to the human body in order to promote meaningful clinical improvements or achieve targeted physiological goals. To this end, my work is focused on the design and experimental evaluation of personalized, adaptive control strategies for assistive robotic devices. In this seminar, I will present work that advances our understanding of how to provide robotic assistance to users outside the laboratory environment. As an example of a personalized control system, I will demonstrate that exoskeleton users can quickly and precisely identify features of robotic assistance that they prefer, and highlight characteristics of user preference that make the design of personalized control systems both compelling and challenging. While many assistive robots support users during walking, I will also share insights into how these methods may be applied to wheeled mobility technologies, specifically for children with disabilities. Together, this work supports my future research goal of designing personalized, adaptive control strategies for wearable assistive robots in order to enable people to meet their goals and achieve full participation in their daily lives. Bio: Kim Ingraham (she/her) is a post-doctoral fellow at the University of Washington in the departments of Rehabilitation Medicine and Mechanical Engineering, and is a member of the multidisciplinary group CREATE (Center for Research and Education on Accessible Technology and Experiences). Kim holds degrees in Mechanical Engineering from the University of Michigan (PhD, 2021) and in Biomedical Engineering from Vanderbilt University (BE, 2012). Prior to beginning graduate school, she worked on an interdisciplinary team of scientists, engineers, and clinicians as a Research Engineer at the Shirley Ryan AbilityLab (2012-2015). Kim is passionate about using assistive wearable robots to advance human mobility in all forms. In her research, she has developed and evaluated physiologically-inspired control systems for a variety of assistive devices, including powered lower-limb prostheses, robotic exoskeletons, and powered wheelchairs for young children with disabilities. Kim is an NSF graduate research fellow, and served as both a graduate student instructor and an engineering teaching consultant at the University of Michigan. =========== Ross L. Hatton Associate Professor, Robotics and Mechanical Engineering Collaborative Robotics and Intelligent Systems Institute Oregon State University coris.oregonstate.edu research.engr.oregonstate.edu/lram/ rosslhatton.com ross.hatton(a)oregonstate.edu

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