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Abstract:Successful human-robot collaboration depends on cohesive communication and a precise understanding of the robot's abilities, goals, and constraints. While robotic manipulators offer high precision, versatility, and productivity, they exhibit expressionless and monotonous motions that conceal the robot's intention, resulting in a lack of efficiency and transparency with humans. In this work, we use Laban notation, a dance annotation language, to enable robotic manipulators to generate trajectories with functional expressivity, where the robot uses nonverbal cues to communicate its abilities and the likelihood of succeeding at its task. We achieve this by introducing two novel variants of Hesitant expressive motion (Spoke-Like and Arc-Like). We also enhance the emotional expressivity of four existing emotive trajectories (Happy, Sad, Shy, and Angry) by augmenting Laban Effort usage with Laban Shape. The functionally expressive motions are validated via a human-subjects study, where participants equate both variants of Hesitant motion with reduced robot competency. The enhanced emotive trajectories are shown to be viewed as distinct emotions using the Valence-Arousal-Dominance (VAD) spectrum, corroborating the usage of Laban Shape.

Biography: I am currently a 3rd year PhD student at the Human Interaction and Robotics (HIRO) Group advised by Prof. Alessandro Roncone. I am passionate about enabling robots to be expressive via movement, resulting in fruitful human-robot collaboration. In my free time, I love dancing, volunteering and playing video games!

Abstract:Multi-robot systems hold significant promise for large-scale mapping, exploration, and search-and-rescue. By combining perspectives, they can cover more ground, build richer maps, and provide greater resilience than single-robot systems. This potential, however, is often limited by communication bandwidth and fragile inter-robot data association, making it difficult to reliably align and share maps. This talk introduces a dataset collected on the University of Colorado campus to examine these challenges and evaluate alignment strategies in realistic conditions. It also presents a compact map representation that emphasizes features especially important for navigation, such as roads and buildings. Lidar data is aligned with OpenStreetMap (OSM) to train a 3D segmentation model capable of directly recognizing these semantics. At runtime, the network generates OSM-like skeletons without requiring external maps, producing representations that are compact, stable, distinctive, and easy to share across robots. Benchmark comparisons with state-of-the-art methods show reduced communication load and improved cross-robot data association. Collaborative mapping experiments further demonstrate the effectiveness of this approach in enabling efficient and robust multi-robot perception.

Biography: Doncey is a Computer Science PhD candidate at the ������ý���� in the Autonomous Robotics and Perception Group (ARPG). His research focuses on navigation and mapping for multi-robot systems, while also maintaining strong interests in field robotics and system-level verification. He earned a B.S. in Mechanical Engineering from Colorado State University in 2021, where he specialized in model-based control and mechatronic system design.

Abstract:Flexible, soft-bodied robots have shown great promise for applications requiring safe and adaptive interaction with complex environments and the people within them. However, their inherent flexibility introduces significant challenges in both design and control, making them more complex than traditional rigid-bodied robotic systems. Motivated by the partial differential equations (PDEs) that govern the motion of soft robotic systems, this talk presents a mathematical framework for the co-design of system dynamics and control laws. The focus is on selecting design parameters and controllers to enhance performance, efficiency, and the potential for distributed implementation. Analytic results are presented for a linear wave equation, while approximate solutions are explored for a nonlinear wave equation. Finally, simulations of a soft robotic crawler navigating a sewer pipe illustrate the practical implications of the proposed approach.

Biography: Emily Jensen is an Assistant Professor in the Department of Electrical, Computer and Energy Engineering at the ������ý����, with an affiliated appointment in the Robotics Program. She received her B.Sc. in Engineering Mathematics and Statistics from the University of California, Berkeley in 2015, and her Ph.D. in Electrical and Computer Engineering from the University of California, Santa Barbara in 2020. Before joining CU ������ý����, she held postdoctoral research positions at UC Berkeley and Northeastern University. Dr. Jensen is a recipient of the UC Regents’ Graduate Fellowship (2016) and the Zonta Amelia Earhart Graduate Fellowship (2019). Her research interests include optimal and robust control, as well as the co-design of multi-agent systems and distributed parameter systems.

Abstract:For individuals with unilateral transtibial amputation, powered ankle-foot prostheses have the potential to reduce metabolic rate of walking, which could contribute to improvements in mobility and quality of life; however, physiological improvements have not been consistently demonstrated in experimental studies. To improve our understanding of the biomechanical mechanisms that drive metabolic rate outcomes, we used a machine learning approach to model the relationship between multimodal biomechanical factors and the metabolic rate of walking with a powered ankle-foot prosthesis. Our model included 49 features describing spatiotemporal parameters, step-to-step transition work, joint kinematics, muscle activity, ground reaction forces, prosthesis settings, and subject characteristics, with a pseudo-R2 of 0.988. The features with the largest effect on metabolic rate were unaffected-to-affected side step length, leading affected leg positive work, integrated unaffected side biceps femoris muscle activity, and peak unaffected side ankle inversion angle. Accumulated local effects plots were used to visualize the direction and magnitude of the relationship between each feature and the metabolic rate of walking. This work furthers our knowledge about the biomechanical and physiological response to powered ankle-foot prosthesis use and could assist in developing new gait strategies to drive reductions in metabolic rate.

Biography: Mikayla is a 3rd year PhD student in the mechanical engineering department. Originally from the Philadelphia suburbs, she completed her undergraduate degree at the University of Notre Dame and master's degree at Carnegie Mellon University, where her research focused on wearable sensors for natural environment biomechanics monitoring. Her current work in the Welker Lab at CU ������ý���� aims to improve the metabolic rate of gait with powered prostheses through machine learning modeling and musculoskeletal simulation tools. Outside of research, she races competitively in the 800m-5k and also loves biking, reading, and cooking/baking.

Abstract:In human-robot collaboration, robots must make sense of inherently noisy, uncertain, and subjective human behavior. This talk explores how environment design can be leveraged to improve robot understanding and interaction with humans across two distinct domains: human motion prediction and reward learning through preferences. In the first part, we address the challenge of goal prediction during collaborative tasks. We propose a method that optimizes shared workspaces—via object placement and virtual obstacles rendered in augmented reality—to induce legible human motion. By formalizing goal legibility as a probabilistic function of trajectory observability and employing quality-diversity search (MAP-Elites), we discover workspace configurations that maximize early goal disambiguation. In the second part, we introduce CRED: a counterfactual reasoning and environment design framework for active preference learning. Standard APL methods often underexplore the trajectory space or fail to generalize across environments. CRED addresses this by (1) sampling diverse reward hypotheses from a belief distribution to generate counterfactual trajectories, and (2) optimizing environment parameters via Bayesian Optimization to construct informative, diverse query contexts. Together, these approaches show that environment design—whether physical or virtual—can be used to proactively shape human behavior and enhance robot inference capabilities in complex, uncertain, and personalized interaction settings.

Biography: Yi-Shiuan Tung is a Ph.D. student in Computer Science at the ������ý����, advised by Professors Alessandro Roncone and Bradley Hayes. His research explores how environment design can be leveraged to enhance predictability, legibility, and reward learning in human-robot interaction. Prior to his doctoral studies, Yi-Shiuan earned his M.Eng. and B.S. in Electrical Engineering and Computer Science from MIT, where he conducted research in human-robot collaboration with Professor Julie Shah.

Abstract: Hummingbirds have evolved unparalleled aerial abilities that are highly sought after in the design of robotic fliers. It is broadly understood that acrobatic maneuvers require agility, but equally important yet less understood, is the need for stability. Stability is vital for their survival and reproduction, often tested in high-speed courtship displays, aerial combat, escape flights, and foraging in windy conditions. In contrast, robotic fliers such as quadcopters or flapping-wing robots often struggle with altitude loss, drifting, or wobbling when executing comparable maneuvers or contending external disturbances in windy conditions. Here, by subjecting the bodies of hovering hummingbirds to quantifiable external disturbances, we show that hummingbirds rejected external roll disturbance torque within merely a single wingbeat (17 ± 4 ms), and arrested roll in three wingbeats (52 ± 8 ms). ��Hummingbirds’ initial response to the disturbance was not actively creating a counter body torque via altering their wing motion patterns. Instead, they maintained wing motion patterns nearly unchanged under external disturbances. Such dynamic stability between wing-body joints allowed hummingbirds to harness aero-inertial Flapping Counter-Torque (FCT) to act against the disturbances near instantly via substantial damping. We hypothesize that hummingbirds achieve this through self-stabilizing properties of their wing musculature or spinal-level reflexes, providing dynamic joint viscoelasticity. ��Our results provide novel insight into how passive or peripheral neural control contributes to whole-body aerial stability, whereas the pathways involving cortical and cerebellar input likely lack the necessary response speed. This novel insight will inform the design of next-generation drones, bringing us closer to replicating the aerial prowess of natural flyers.

Biography: Dr. Cheng is an Associate Professor of Mechanical Engineering at Penn State. Prior to this, he was a Postdoctoral Research Associate in the School of Mechanical Engineering at Purdue University, where he received his Ph.D. in 2012. He also received his M.S. in Mechanical Engineering from University of Delaware and B.S. from Control Science & Engineering at Zhejiang University, China. His research interests lie in the broad science and engineering of efficient, robust and agile locomotion in fluids, including animal flight, fish swimming, robot locomotion and learning and biologically inspired fluid dynamics. Working in a highly interdisciplinary field, Dr. Cheng's work has been published in journals from various disciplines, such as Science, Science Advances, Journal of Fluid Mechanics, Physics of Fluids, IEEE Transaction on Robotics, Proceedings of Royal Society B, Journal of Experimental Biology, and Journal of the Royal Society Interface. His research has been funded by various programs of National Science Foundation (NSF), Army Research Office (ARO), Office of Naval Research (ONR), and Air Force Office of Scientific Research. Dr. Cheng received NSF CAREER Award in 2016.

Abstract: Recent advances in machine learning, computer vision, sensors, and computing have opened up opportunities to develop practical assistive technologies for real-world applications. However, these technologies have traditionally underutilized key algorithms and techniques from robotics. Concepts from robotics, such as persistent mapping, modeling guidance as a Markov Decision Process, and social guidance, are essential for building effective assistive autonomy. This talk will explore some of these use cases and demonstrate how robotics principles can transform assistive technology into a tool for long-term guidance and independence.

Biography: I am a final year CS Ph.D. student with Prof. Bradley Hayes at CU ������ý����. I am interested in Robotics, Accessibility, and Human Robotics Interaction (HRI) and unifying them to create real-world Assistive Technology. My thesis involves developing robotic systems that can assist people with visual impairments in performing daily tasks more independently by providing long-term and fine-grain guidance.

Abstract: Erin will present her research focused on improving human-autonomy teaming for operational scenarios such as human spaceflight. Future autonomous systems onboard space habitats may serve as better teammates to human operators by leveraging predictions of cognitive states to intelligently adjust their behavior. Current approaches of estimating cognitive states, such as surveys or behavioral measures, are obtrusive, task-specific, or cannot be used in real-time. Physiological modeling, where biosignals are used to predict operator cognitive states, has the potential to overcome these limitations. Erin’s research develops predictive models of cognitive states based on physiological data, aiming to inform adaptive autonomous systems and mitigate health and performance decrement. She explores trading operational utility for model accuracy and also investigates whether physiological models can transfer between spaceflight-related tasks.

Biography: Erin Richardson is a PhD student in Bioastronautics at the ������ý���� researching human performance and human-autonomy teaming in extreme environments such as outer space. She completed her undergrad in Engineering Science at the University of Toronto with a major in Aerospace Engineering and a minor in Robotics and Mechatronics and her Master’s in Aerospace Engineering Sciences at CU ������ý���� with a Certificate in Data Science. Excited by all things space, Erin has also studied the effects of microgravity on the human body on a parabolic flight, completed her Private Pilot License, and learned to scuba dive. She is passionate about empowering youth to pursue STEM pathways and enjoys volunteering with the Canadian Association for Girls in Science and Let’s Talk Science.

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Abstract: Insect olfactory systems offer unparalleled sensitivity and specificity in detecting volatile organic compounds (VOCs), often outperforming synthetic sensors in both speed and selectivity. Electroantennography (EAG) is a classical method for capturing olfactory neural activity, but traditional approaches suffer from limited spatial resolution, single-signal output, and short-lived antennal tissue viability. In this talk, I present a modernized approach that leverages multielectrode arrays (MEAs) to spatially resolve olfactory sensory neuron (OSN) activity across the antenna, significantly enhancing chemical classification through machine learning-based signal decoding. Using Manduca sexta as a model, we demonstrate that MEA-based EAG captures high-resolution spatiotemporal neural signals capable of distinguishing VOCs from 11 chemical classes with ≥99% accuracy in controlled settings. To overcome the temporal limitations of excised antennal tissue, we introduce a microfluidic support platform that maintains tissue viability for over 15 hours, enabling sustained and repeatable measurements compatible with biohybrid autonomous systems. These innovations open new avenues for field-deployable chemical sensing in defense, environmental, and public health applications, offering real-time, preparation-free VOC detection with sub-second response times. This work redefines insect olfaction as a scalable, integrative biosensing platform ready to augment or surpass conventional technologies.

Biography: Elisabeth Steel serves as a Senior Research Engineer and Principal Scientist for the Biohybrid Sensing program in the Air and Space Biosciences Division in the 711th Human Performance Wing at Air Force Research Laboratory in Dayton, Ohio. Concurrently, Dr. Steel supports novel sensor and device design, fabrication, testing and evaluation for the AFRL Operational Sensing and Force Health Protection sections as a BlueHalo Technical Program Manager. Her expertise is in neural tissue engineering and electrophysiology with recent focus in the design and testing of biohybrid gas sensors inspired by insect olfaction. Dr. Steel conducted neuromodulation research as a post-doctoral fellow with Dr. Tim Bruns at the University of Michigan. Additionally, she served as a Technical Sales Engineer with NeuroNexus Technologies, the industry leader in microelectrode array technology for neuroscience electrophysiology applications. She received her PhD (2018) and M.S. (2013) in Biomedical Engineering from Wayne State University in Detroit, Michigan. She completed her B.S. in Bioengineering in 2007 from University of Toledo. Dr. Steel draws on diverse domains -- including physiology, cell biology, electrophysiology, microfluidics, materials science, electronics, and AI/ML-- to create innovative, real-world solutions for chemical detection and environmental sensing. Dr. Steel's interdisciplinary approach is driven by a passion for transforming fundamental biological processes into scalable technologies for defense, security, and public health applications.

Abstract: Using odor to locate a person, item, or thing is an underutilized task. In life-saving scenarios, such as in search-and-rescue, we use nature in the form of search dogs to locate trapped people via smell because man-made sensors are not portable enough or sensitive enough to be used in this way. By taking advantage of the moth’s incredible antennae, we can make odor localization a more widespread task. During her doctoral work, Dr. Anderson designed miniaturized circuitry which can read the signals from a moth antenna (electroantennogram) and interface with a pocket-sized drone platform. This drone, dubbed the Smellicopter, is programmed to search for odors autonomously using a bio-inspired search algorithm and is equipped with wind-vane-like fins for passive upwind orientation. She continues to further this technology as a Washington Research Foundation Postdoctoral Fellow by collaborating with other researchers in the Riffell Lab at the University of Washington to apply Gene Editing and Machine Learning techniques to differentiate between chemicals using the moth antenna.��

Biography: Melanie Anderson is a Washington Research Foundation postdoctoral fellow in the Biology Department at the University of Washington. Her research focuses on biohybrid robotic systems, especially those that fuse multisensory input towards fully autonomous operation. She received her PhD in Mechanical Engineering in 2021 from the University of Washington, where she created the Smellicopter, a biohybrid palm-sized drone capable of autonomously seeking out the source of an odor using a live moth antenna as an onboard chemical sensor.��

Abstract: PCBs are used in all electronic systems. Sometimes the interconnects are transparent, in which case you are only designing for manufacturability. But when the interconnects affect performance, there are a few guides to follow to achieve the lowest noise PCBs. Some of these guidelines will be presented and why, based on applying electrical engineering principles.

Biography: Eric received his BS in physics from MIT in 1976 and PhD in physics from the University of AZ in Tucson in 1980. After 40 years in industry, he has been teaching the ECEE capstone course and graduate courses in signal integrity in ECEE for the last 4 years.��

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Abstract: Unsurprisingly, the environments encountered for space robotics applications are very different than our standard terrestrial applications. This can lead to very unintuitive and specialized designs for robotic missions. In this talk, I will present one such example of a robotic platform that we have designed at CU ������ý���� called Area-of-Effect Softbots (AoES). AoES are specifically designed to be deployed in orbit about a small asteroid, solar sail down to the surface of the asteroid, where they can safely land and operate on the surface. The original purpose of AoeS was to enable a new concept for material extraction - ie mining - on the asteroid surface. Carrying out this mission requires a unique anchoring approach, as well as mobility to move across the surface. After explaining the design and application, we will discuss some other possible applications for AoES on asteroids and beyond.��

Biography: Jay McMahon is an Associate Professor in the Ann and H.J. Smead Aerospace Engineering Sciences department at the ������ý����. His research focuses on autonomy, guidance, navigation, and control for spacecraft, along with the governing dynamics for these systems. He has especially focused on applications to small bodies. He has been a part of NASA's DART, Janus, and OSIRIS-REx mission, the Emirates Mission to the Asteroid Belt, JAXA's Hayabusa2 mission, and ESA's Hera mission. He obtained his PhD from the ������ý���� in 2011, his MS in 2006 from the University of Southern California, and his BS from the University of Michigan in 2004. He previously worked on launch vehicle guidance systems at The Aerospace Corporation in El Segundo, CA. Asteroid (46829) McMahon - a main belt binary asteroid - is named in his honor.

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Biography: Morteza Lahijanian is an assistant professor in the Aerospace Engineering Sciences department, an affiliated faculty at the Computer Science department and Robotics program, and the director of the Assured, Reliable, and Interactive Autonomous (ARIA) Systems group at the ������ý����. He received a B.S. in Bioengineering at the University of California, Berkeley and a PhD in Mechanical Engineering at Boston University. He served as a postdoctoral scholar in Computer Science at Rice University. Prior to joining CU ������ý����, he was a research scientist in the department of Computer Science at the University of Oxford. His awards include Outstanding Junior Faculty, Ella Mae Lawrence R. Quarles Physical Science Achievement Award, Jack White Engineering Physics Award, NSF GK-12 Fellowship, and Wadham College Research Fellowship. Dr. Lahijanian's research interests span the areas of control theory, stochastic hybrid systems, formal methods, machine learning, and game theory with applications in robotics, particularly, motion planning, strategy synthesis, model checking, and human-robot interaction. His lab develops novel theoretical foundations and computational frameworks to enable reliable and intelligent autonomy. The emphasis is especially on safe autonomy through correct-by-construction algorithmic approaches.

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Biography: Qi Heng is PhD Student in aerospace engineering sciences at the ������ý����. He is advised by Morteza Lahijanian and Zachary Sunberg. His research interests lie at the intersection of formal methods and planning under uncertainty. My goal is to enable safety-critical autonomous partially observable cyber-physical systems under uncertainty to complete complex temporal tasks while providing explicit guarantees on their safety and operational properties.

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Abstract: The ability to manufacture microscale sensors and other components for robotic systems has intrigued the robotics community for almost 40 years. There have been huge success stories; MEMS inertial sensors have enabled an entire market of low-cost, small-scale UAVs. However, the promise of ant-scale robots has largely failed. Ants and other small insects like mites can move at high speeds on surfaces from picnic tables to front lawns, but the few legged microrobots that have walked have typically done so at slow speeds on smooth surfaces. Similarly, the vision of large numbers of microfabricated sensors in 'skins' that help robots interact directly with their environment has suffered in part due to the brittle materials and size constraints in micro-fabrication. This talk will present our progress in the design of sensors, mechanisms, and actuators that utilize new microfabrication processes to incorporate materials with widely varying moduli and functionality to achieve more robustness, dynamic range, and complexity in smaller packages. Results include arrays of strain and flow sensors that can be used for more agile autonomous flight as well as legged microrobots down to 1 milligram that provide insights into simple design and control for high speed locomotion in small-scale mobile robots.

Biography:Sarah Bergbreiter joined the Department of Mechanical Engineering at Carnegie Mellon University as a Professor in the fall of 2018 after spending ten years at the University of Maryland, College Park. She started her academic career with a B.S.E. degree in electrical engineering from Princeton University in 1999. After a short introduction to the challenges of sensor networks at a small startup company, she received her M.S. and Ph.D. degrees from the University of California, Berkeley in 2004 and 2007 with a focus on small-scale robots. Prof. Bergbreiter received the DARPA Young Faculty Award in 2008, the NSF CAREER Award in 2011, and the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2013 for her research on engineering��robotic��systems down to millimeter size scales. She has received several Best Paper awards at conferences like ICRA, IROS, and Hilton Head Workshop with her fabulous group of students and former students, and is a Fellow of the ASME. She also served as Vice Chair of DARPA’s��Microsystems��Exploratory Council from 2020 through 2022. Outside of academia, she enjoys spending time with her husband and two daughters, running long-ish distances outside rather slowly, and the rare game of water polo.

Abstract: When human operators of cyber-physical systems encounter surprising behavior, they often consider multiple hypotheses that might explain it. In some cases, taking information-gathering actions, such as additional measurements or control inputs given to the system can help resolve uncertainty and determine the most accurate hypothesis. The task of optimizing these actions can be formulated as a belief-space Markov decision process that we call a hypothesis-driven belief MDP. Unfortunately, this problem suffers from the curse of history similar to a partially observable Markov decision process (POMDP). To plan in continuous domains, an agent needs to reason over countlessly many possible action-observation histories, each resulting in a different belief over the unknown state. The problem is exacerbated in the hypothesis-driven context since each action-observation pair spawns a different belief for each hypothesis, leading to additional branching. In this talk I will present a new belief MDP formulation that: (i) enables reasoning over multiple hypotheses, (ii) balances the goals of determining the (most likely) correct hypothesis and performing well in the underlying POMDP, and (iii) can be solved with sparse tree search. Time permitting, I will discuss our recent work on leveraging LLMs for interactive transition from hypothesis to POMDP model.

Biography:Ofer Dagan received the B.S. degree in aerospace engineering, in 2010, and the M.S. degree in mechanical engineering, in 2015, from the Technion - Israel Institute of Technology, Israel, and the Ph.d. degree in aerospace engineering with the Ann and H.J. Smead Aerospace Engineering Sciences Department, ������ý���� in 2024. He is currently a postdoctoral fellow at the Autonomous Decision and Control Lab (ADCL) at the ������ý����. From 2010 to 2018 he was a research engineer in the aerospace industry. His research interests include theory and algorithms for decentralized Bayesian reasoning and decision-making in heterogeneous autonomous systems.

Abstract: Active particles are heavily investigated for use within microrobotics, biosensing, and functional materials due to their ability to harness energy from their environment to achieve higher-order assembly or locomotion. The behavior of these particles requires some form of asymmetry either in their overall shape and/or in their surface by the addition of surface patches to asymmetrically dissipate energy. Patch asymmetries are typically produced using head-on or glancing-angle metal deposition, both of which limit the extent of accessible patch shapes due to the lack of spatial control. Shape asymmetric particles are often made using photolithography. Traditional methods such as contact- or maskless-lithography are limited to two-dimensional asymmetries. While these approaches have enabled researchers to study and highlight the importance of understanding the structure-function relationship within simple active particles, the ability to access the entire range of particle and patch asymmetries remains limited by current fabrication methods. Thus, we developed a fabrication method by which we can deposit arbitrary, well-defined metallic patches onto polymeric particles of any three-dimensional shape. Using two-photon lithography, we simultaneously fabricated our desired particles and separate, removable stencils with a specific patch design. We then deposited our metal of choice using directional evaporation and removed the stencils, leaving behind an array of precisely designed active particles. To demonstrate the versatility of this platform, we fabricated particles possessing unique shapes and metallic patches for different applications. We highlight the effects of additional axes of asymmetry and decreasing patch surface area on the propulsion of spherical particles within an electric field by depositing complex gold surface patches. We showcase the effects of adding “internal” shape asymmetries by depositing platinum patches within particle cutouts to perturb asymmetries about the axis of a given particle when exposed to a hydrogen peroxide solution. Finally, we report the ability to design particles of any shape to control assembly by deviating from rounded particles and instead depositing magnetic patches onto L-shaped particles so that their assembly is restricted to dimers. In all these cases, achieving finer and more versatile control over asymmetry enabled the potentially accessible range of trajectories and assemblies to be greatly widened and more precisely controlled.

Biography:Kendra Kreienbrink is originally from Minnesota. She got her B.S. from the University of Wisconsin- La Crosse in Biomedical Physics with minors or Chemistry and Mathematics. At CU she is a graduate student in Materials Science and Engineering and Interdisciplinary Quantitative Biology. She is currently working in Dr. Wyatt Shields’ lab where she is researching fundamental mechanisms of how active particles and microrobots work, are made, and how they could applied to areas like drug delivery or environmental remediation. Kendra makes sure to balance work with many outdoor activities such as skiing, running, biking and hiking as well as recreational team sports.

Abstract: Robots in collaborative workspaces are generally very of human beings and objects that they shouldn't manipulate. In this talk, we demonstrate methods by which robots can be made contact friendly, and use that to their advantage in dealing with complex environments.

Biography:Yaashia Gautam is a PhD student in the Department of Electrical, Computer and Energy Engineering, under the supervision of Dr. Marco Nicotra. She is a part of the ROCC lab and an external collaborator to the HIRO lab. Her interests include robotic controls and physical Human Robot Interaction.

Abstract: Control barrier functions (CBFs) have recently garnered attention from the constrained control community by serving as both a certificate of safety and a tool for synthesizing constrained control laws. The principle behind CBF-based control is to select an input that is as close as possible to a nominal control action while also guaranteeing constraint enforcement. Due to their conceptual simplicity, computational efficiency, and overall performance, CBF-based controllers have been implemented successfully on a wide variety of applications. However, designing CBFs is challenging and can be compared to the task of designing Lyapunov functions to certify stability. In this talk, I will explain how CBFs can be used in practice to achieve safety, and present some of our results for their systematic construction.

Biography:Victor Freire received the B.S. and M.S. degrees in mechanical engineering from the University of Wisconsin–Madison, Madison, WI, USA, in 2020 and 2022, respectively. He is currently pursuing the Ph.D. degree from the Department of Electrical, Computer and Energy Engineering, ������ý����, ������ý����, CO, USA. He joined the Robotics, Optimization and Constrained Control (ROCC) Lab under Professor Marco Nicotra's direction in 2022. His research interests include constrained control, UAV systems, and trajectory planning.

Abstract: Insect antennae are incredible distributed biological sensors that provide��rich spatial and temporal multimodal information about the surrounding environment, enabling informed decision making even in poorly lit conditions. While high-fidelity tactile perception is common in insects, there is no engineering analogue for insect-scale robots due in part to their strict size, weight and power budgets, limiting their capacity for autonomous navigation. The American cockroach (Periplaneta americana) antenna-with its approximately 140 strain sensing segments is one such example of a highly integrated and distributed mechanosensory system enabling tactile navigation with minimal overhead in weight and energy consumption. Inspired by this highly capable biological system, our work is aimed at designing a near scale multi segmented compliant robophysical antenna with integrated capacitive sensing capable of measuring individual hinge deflection with high accuracy and speed. The antenna is constructed using the stack laminate approach, which has been successfully applied to actuators, sensors, and hinges in a variety of millimeter scale systems. This robophysical system will ideally enable biologists to validate mechanical principles governing tactile sensing and roboticists to achieve autonomous touch-based navigation at the insect scale.

Biography:Parker McDonnell is a fourth year PhD student in the MCEN department at CU ������ý����, researching bioinspired robotics with a focus in power and sensing autonomy. Parker grew up in the distant land of New England where he completed his undergraduate and masters degree in electrical engineering at the University of New Hampshire. After working four years in the aerospace industry and a short break to travel in Southeast Asia he moved to Colorado to pursue a PhD. When not working, Parker is typically found mounting biking with friends at Trestle bike park or climbing in ������ý���� canyon on summer weekends and snowboarding in the winter.

Abstract: Kinematic singularities in robots are generally problematic. There are two main types of singularities. For the first type, a robot loses its ability to exert forces in a certain direction. For the second type, a robot loses its ability to move in a certain direction. Therefore it is best that singularities be avoided. This presentation takes a backward approach. Singularities are embraced, and we explore what extra functionalities might be obtained by purposefully designing singularities into the configuration space. This act of design is nonelementary. We take a computational approach, one based in root-finding. The root-finding problems posed can be huge. Therefore, we have pushed the bounds in coming up with more capable root finding algorithms. Results are packaged into visualizations suitable for design space exploration.

Biography:Mark Plecnik is an assistant professor in the Department of Aerospace and Mechanical Engineering at the University of Notre Dame. His research is grounded in the computational design of robot geometries. His current focus is in forming the prevailing dynamics of a robot by shaping its configuration space rather than depending purely on motor control to achieve desired motions. Plecnik received the NSF CAREER Award in 2022. He received the Freudenstein Young Investigator Award from ASME in 2024. He is a senior member of IEEE.

Abstract: Caleb will present real-time control methods for robot collision avoidance around dynamic objects and how to perceive objects in real-time. Sensors are required to detect obstacles near a robot manipulator, we look at information originating from the robot body itself through on-board sensors. For robots to be physically located within human laden environments, advances are needed in both control and sensing technology.

Biography:Caleb Escobedo is a member of the Human Interaction and Robotics (HIRO) group, advised by Dr. Alessandro Roncone at CU. His research focuses on novel close-proximity (< 15cm) sensor development and sensor integration with dynamic robot arm movement. He spent a year in the middle of his PhD working at the Samsung Artificial Intelligence Center – New York on the Novel Sensors Team. Caleb leads the HIRO Robotic Skin Project in development of software and hardware required for full-surface robot close-proximity and contact detection.

Abstract: Motion planning is a fundamental technique in robotics that bridges the gap between high-level task specifications and low-level control commands. The field has evolved to encompass a broad array of algorithms across several classes---sampling-based, optimization-based, learning-based, and combinations thereof---each offering trade-offs in completeness, optimality, and computational efficiency. As robotic systems expand into increasingly unstructured real-world environments, addressing the critical issue of generalization---the ability of these algorithms to perform effectively in previously unseen scenarios---becomes paramount for maximizing their utility. In this talk, we explore the use of denoising diffusion models, the technology underpinning generative art tools like Midjourney and Sora, in robotics. We specifically examine how these models can complement sampling-based motion planning methods and emphasize how the capacity for continual improvement sets learning-based approaches apart from traditional techniques. This becomes particularly crucial as robots are increasingly deployed in diverse real-world settings, highlighting the need for addressing generalizability. Additionally, we discuss the challenges associated with diffusion-based planning methods and highlight some of the ongoing work in this area.

Biography:Anuj Pasricha is currently pursuing his PhD under Dr. Alessandro Roncone in the Human Interaction and RObotics [HIRO] Group at the ������ý����. His research focuses on maximizing the utility of existing robot embodiments (specifically, manipulators) through: i) kinodynamic planning for nonprehensile manipulation, and ii) hybrid approaches combining traditional motion planning with machine learning techniques. He holds a Bachelor's in Computer Engineering from the University of Illinois at Urbana-Champaign.

Abstract: As technology advances, swimming robots and bioinspired design in fluid dynamics can potentially be used for monitoring the ocean, performing tasks in remote locations, and other practical applications. Looking towards nature for inspiration can address some of the grand challenges of robotics, such as improved dexterity and adaptive abilities in unstructured environments. This work presents examples of bioinspired swimming robots using approaches that combine laboratory and field experiments, theoretical models, and computational fluid dynamics. First, we demonstrate a biohybrid robot that uses a microelectronic system to induce swimming in live jellyfish in the laboratory and ocean. Using entirely synthetic materials, we also address how bioinspired sharkskin surfaces and robotic fish fins can improve the performance envelope of vehicles. Future applications include improving swimming speeds, efficiencies, and antifouling properties for enhanced persistence. These examples provide a strong foundation for continued work to design and implement robots and biofluids for real-world applications.

Biography:Dr. Xu is an Assistant Professor in the Paul M. Rady Department of Mechanical Engineering. Prior to her appointment, she was a National Research Council (NRC) Postdoctoral Research Associate in the Laboratories for Computational Physics & Fluid Dynamics at the U.S. Naval Research Laboratory (NRL) in Washington, D.C. She received her Ph.D. in Bioengineering from Stanford University, M.S. in Bioengineering from the California Institute of Technology, and B.S.E. in Bioengineering from the University of Pennsylvania. Her research focuses on the intersection of robotics, fluid dynamics, and biology, and the lab's mission is to develop and deploy bioinspired aquatic robots for real-world applications using a combination of laboratory experiments, theoretical modeling, and field work. By combining features from both natural and engineered designs, the lab aims to create more energy-efficient, maneuverable, and robust robots and underwater vehicles to track climate change, observe natural phenomena in the ocean, and aid in environmental stewardship. Dr. Xu will teach a new course in Spring 2025 on "Biohybrid Robotics: Organic Machines," which will feature lectures and hands-on labs with electrode-driven jellyfish swimming and hawkmoth antennae-sensing aerial drones.

Abstract: During this session we will go through the process of designing autonomous systems for space applications. Starting from conceptualization and all the way to deployment, we will examine each of the necessary steps to achieve mission success.

Biography:Luke Bowersox is a robotics engineer with a background in electrical engineering, specializing in robotics from the Colorado School of Mines. Since 2019, he has worked at four different startups in Colorado, where his passion for R&D has driven him to architect, build, and test systems destined for the moon and asteroids. Throughout his career, Luke has built rovers, drones, perception systems, navigation stacks, and manipulators.

Abstract: The future of deep space exploration will take science missions far beyond Earth orbit onto the surfaces of the Moon and Mars, into the atmospheres of other planets, and onto the surfaces of asteroids and outer solar system icy moons. With these increasing distances arise new demands for autonomous robots that reliably reason and make decisions under uncertainty, especially in situations which human ground-based control teams and astronaut crews will not always be able to anticipate or control. This talk will present new approaches to tackling such problems that are rooted in recent advances for Bayesian probabilistic artificial intelligence. Among these are bandit-based learning techniques for scientific data collection based on the theory of Active Inference, a computational neuroscience inspired framework in which agents can naturally balance exploration and exploitation in uncertain surroundings using a probabilistic free-energy minimization principle. Results for simulated geological survey missions show that bandit exploration algorithms based on Active Inference significantly outperform many other conventional bandit-based learning strategies, while also accounting for human scientist preferences. Techniques for introspective competency self-assessment will also be presented for autonomous robots to identify and communicate their capabilities in uncertain task settings via “machine self-confidence”, which depends on decision-making statistics that are easily computed from probabilistic decision-making and planning algorithms. Some recent results will be discussed for deploying our machine self-confidence assessment and reporting framework in analog rover-based exploration settings such as the ASPEN Lab at CU ������ý���� and the Mars Desert Research Station in Utah.

Biography: ��Nisar Ahmed is an Associate Professor in the Smead Aerospace Engineering Sciences Department at the ������ý����. He is Director of the Research and Engineering Center for Uncrewed Vehicles (RECUV) and directs the Cooperative Human-Robot Intelligence (COHRINT) Lab. He received his B.S. in Engineering from Cooper Union in 2006, his Ph.D. in Mechanical Engineering from Cornell University in 2012 through an NSF Graduate Research Fellowship, and he was a postdoctoral research associate in the Cornell Autonomous Systems Lab from 2012 to 2014. He was awarded the 2011 AIAA Guidance, Navigation, and Control Conference Best Paper Award; an ASEE Air Force Summer Faculty Fellowship in 2014; the 2018 Aerospace Control and Guidance Systems Committee (ACGSC) Dave Ward Memorial Lecture Award; and Smead Aerospace Faculty Fellowship (2021-2023) and Outstanding Graduate Teaching and Mentorship Award (2021). His work has been supported by the Army, Air Force, DARPA, Navy, NASA, Space Force, and multiple industry sponsors. He has organized several international workshops and symposia on autonomous robotics, sensor fusion, and human-machine interaction. He is a Member of the IEEE and the AIAA Intelligent Systems Technical Committee, and he is the CU Site Director of the NSF IUCRC Center for Aerial Autonomy, Mobility, and Sensing (CAAMS).

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Biography: TBA

Abstract: Miniature robots provide unprecedented access to confined environments and show promising potential for novel applications such as search-and-rescue and high-value asset inspection. Current robots (especially at the insect scale) cannot modify their shape to significantly improve performance or add new functionality. Insects demonstrate additional skills through exoskeletal body compliance, enabling locomotion through environments smaller than body size. Yet, the change in scale, and use of exoskeletal shape morphing enables observation of nature with a new perspective. We have developed robots out of individual leg modules containing actuators and sensing. The modular robot structure has passive inter-segment flexures for body compliance. Spherical five-bar linkages are used for leg joint mechanics, with optimized actuation for additional payload. Each leg tip has two independent degrees of freedom. The exoskeleton shape morphing design of our robot has demonstrated omnidirectional locomotion in variety of environments and running on a variety of different surfaces. Passive body shape-morphing enabled Eclair to maneuver through a lateral constraint, narrower than the neutral body shape. The use of design concepts from exoskeletal interlinked modular robot units enable passive body compliance in insect scale robots to locomote in confined terrain previously not accessible.

Biography: ��Heiko Kabutz received his B.Eng. in Mechanical Engineering from the University of Pretoria, South Africa, in 2019. He received his M.S. in 2022 and is currently pursuing his Ph.D. under Professor Kaushik Jayaram in the Animal Inspired Movement and Robotics Lab at the ������ý����, USA. His research interests include the mechanical design, biomechanics and insect-scale manufacturing of legged movement mechanisms for bioinspired robotics.

Abstract: The state of the art of autonomous robots for exploration has been advanced significantly over the past few years, primarily due to the DARPA Subterranean Challenge. From a technical perspective, the SubT Challenge forced teams to integrate critical stand-alone capabilities such as SLAM and object classification into deployable solutions with both robustness and persistence in austere environments. In this talk we’ll briefly review the details of our solution (Team MARBLE, 3rd place), with a focus on future advancements based on lessons learned from three years of real-world deployments.

Biography:��Prof. Sean Humbert is the Director of the Robotic Program and the Denver Business Challenge Professor in the Department of Mechanical Engineering at the University of Colorado, ������ý����. He received his BS in Mechanical Engineering from the University of California Davis and his MS and PhD degrees in Mechanical Engineering from Caltech. His main research areas are robotics and autonomy, with a focus in perception, reduction and feedback principles in biology. His laboratory works with biologists to apply control- and information-theoretic tools to formalize these principles in small animals such as insects, providing insight into the biology and resulting in novel, robust and computationally efficient solutions for small-scale engineered systems.