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        "time_start":"11:00:00",
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        "time_zone":"America\/Detroit",
        "event_title":"Robotics Fall Demo Days",
        "occurrence_title":"",
        "combined_title":"Robotics Fall Demo Days: End of term projects from the Robotics Department",
        "event_subtitle":"End of term projects from the Robotics Department",
        "event_type":"Exhibition",
        "event_type_id":"7",
        "description":"Check out robotics projects from department courses. Students show off their projects in a variety of ways, including demos, talks, and in competitions. Note: considerable downtime may occur while setting up or cleaning up. Take advantage of our building audio tour (audio.robotics.umich.edu) during breaks.\r\n\r\nWednesday, December 3\r\nROB 340: Human-Robot Interaction \/ Presentations \/ 4:30pm to 6:30pm in FMCRB 2300\r\n\r\nThursday, December 4\r\nROB 550: Robotic Systems Lab \/ Competition \/ 10:30am - 4:30pm in Atrium\r\n\r\nMonday, December 8\r\nROB 450: Capstone \/ Demos \/ 10am - Noon in Atrium\r\n\r\nWednesday, December 10\r\nROB 311: How to Build Robots & Make Them Move \/ Competition \/ 11am - 5pm in Atrium",
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        "event_title":"Leveraging Intentional Collisions to Enhance Aerial Manipulation",
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        "combined_title":"Leveraging Intentional Collisions to Enhance Aerial Manipulation: Mark Nail, PhD Defense",
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        "description":"Committee Chairs: Brent Gillespie and Ella Atkins\r\n\r\nZoom Link: https:\/\/umich.zoom.us\/j\/95579522863\r\n\r\nAbstract: \r\nUncrewed Aircraft Systems (UAS) have become essential in tasks that are too difficult or dangerous for humans. This dissertation was initially inspired by the challenge of making rainforest tree sampling safer for botanists, who often need to climb dangerously tall trees. Building on this motivation, we developed a new approach for aerial manipulation that enables interaction with objects of unknown dynamics through intentional collisions using a pogo-stick payload. The key insight is that intentional collisions consolidate all potential disturbance forces into a single impulse, the outcome of which is hopefully simpler to predict. Given assumptions on the dynamics of the interaction object, the map from pre- to post-impact states (collision response) is known, providing an opportunity to choose pre-impact states that will result in recoverable post-impact states.\r\n\r\nThe use of intentional collisions with a UAS presents unique challenges due to underactuation. Collision outcomes are set by strike velocity and orientation, yet hitting a desired point requires full position control. Traditional UAS actuator configurations cannot provide both full position and attitude control authority. This dissertation presents several approaches to mitigate underactuation and enable aerial manipulation with intentional collisions.\r\n\r\nTo begin, this dissertation develops the strategy of using intentional collisions in a flight mode called Velocity Matching. The Velocity Matching flight mode starts in two phases: launch phase and then the Velocity Matching phase. During the launch phase, the UAS is sped up horizontally to \"launch\" the vehicle, assumed thereafter to behave ballistically, towards the target. After launch, the Velocity Matching phase is entered, and thrust is limited to the bare minimum thrust required for attitude control. During the Velocity Matching phase the minimal thrust attitude control is used to align the center-of-mass velocity vector of the UAS with the pogo-stick\u2019s orientation at strike. Using simulation and flight tests, we generate a map from pre-launch modes states into recoverable post-impact states, allowing for planned strikes.\r\n\r\nSecond, we refine the flight control from Velocity Matching to a cascaded position\u2013attitude switching controller (called Switching Control). In the Switching Control mode, the UAS flies in position cascaded PID control for most of the trajectory, then switches at the last moment to attitude cascaded PID control for pre-strike alignment. We also provide a tool to predict the best switching time from an initial displacement, and we validate the approach experimentally.\r\n\r\nFinally, we develop a new control approach involving an actuated pogo-stick payload controlled via a partial feedback linearizing controller to regulate both UAS position and pogo-stick orientation. After validating the actuated pogo-stick in simulation, we built a physical cable-driven version that slides along a hemispherical base. This mechanism provides two-degree-of-freedom orientation control (pitch and roll compensation) and keeps the pogo-stick perpendicular to the UAS center of mass, enabling more favorable, recoverable collision responses.",
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        "id":"143210-21892426",
        "datetime_modified":"20260105T125309",
        "datetime_start":"20260107T110000",
        "datetime_end":"20260107T130000",
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        "date_start":"2026-01-07",
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        "time_zone":"America\/Detroit",
        "event_title":"Toward Improved Neuroprosthetic Control of Multiple Degrees of Freedom for Fingers and Wrist via Implanted Neural Interfaces",
        "occurrence_title":"",
        "combined_title":"Toward Improved Neuroprosthetic Control of Multiple Degrees of Freedom for Fingers and Wrist via Implanted Neural Interfaces: Robotics PhD Defense, Dylan Wallace",
        "event_subtitle":"Robotics PhD Defense, Dylan Wallace",
        "event_type":"Presentation",
        "event_type_id":"16",
        "description":"Committee Chair: Cynthia Chestek\r\n\r\nAbstract:\r\nModern prosthetic and robotic hands offer significant opportunities to restore limb function for individuals with limb loss or impairment. However, the current standard of control for many of these devices does not provide sufficiently rich control signals to match the number of movements these hands can perform. Implanted neural interfaces offer a promising approach to obtain control signals with substantially higher spatial and temporal resolution than noninvasive technologies. These interfaces have the potential to improve control of multiple degrees of freedom in the fingers and hand, and to incorporate additional degrees of freedom such as the wrist. This dissertation investigates how implanted neural interfaces can be used to improve control of multiple degrees of freedom in the hand and wrist.\r\n\r\nFirst, we examine an augmentation to an intracortical brain\u2013machine interface decoder designed to reduce errors during control of multiple finger groups. Two non-human primates were implanted with intracortical microelectrode arrays in the hand-knob area of primary motor cortex and trained to control a virtual hand using intracortical neural signals. Neural activity from the arrays was associated with finger kinematics to create a decoder controlling the index and middle-ring-small finger groups. Movement errors were classified using the same intracortical data and incorporated into a subsequent closed-loop control session to detect and correct errors in real time. Incorporating error correction resulted in a significantly lower orbiting time around targets, with an average reduction of 26%. This reduction represents an important improvement in control and may provide even greater benefit in human users, where control errors occur more frequently.\r\n\r\nNext, we investigate a peripheral nervous system interface technology rather than intracortical signals. Regenerative Peripheral Nerve Interfaces (RPNIs) provide a rich source of peripheral nerve control signals for individuals with limb loss by amplifying efferent nerve activity for muscles lost with the limb. Two participants with upper limb loss between the elbow and wrist received RPNIs and implanted electrodes in both the RPNIs and residually innervated muscles. Classifiers were trained to decode implanted neural signals into discrete finger and wrist movements, and performance was compared to classifiers trained using surface electrodes. Implanted electrodes provided an average improvement in classification accuracy of over 38% during arm movement. This improvement translated to a reduction in task completion time of over 27% in one participant during an activity of daily living performed using the implanted classifier compared to the surface-based classifier.\r\n\r\nFinally, we evaluated continuous control of the hand and wrist in the same two participants. Participants controlled a virtual hand using a reduced set of hand and wrist rotation targets. Implanted electrodes provided high-correlation control signals for both hand and wrist and outperformed surface electrodes in closed-loop control. In one participant, implanted and surface decoders were trained and tested in both sitting and arm-out-front postures. Surface decoders tested in a posture different from training exhibited an increase in trial time compared to implanted decoders in a different posture, along with a reduction in success rate between surface and implanted decoders.\r\n\r\nTogether, these results demonstrate that implanted neural interfaces enable more precise and accurate control of finger and wrist movements. Future work will focus on further improving control through advanced machine learning methods, improved training data labeling, and translation to fully implantable systems suitable for use outside the laboratory.\r\n\r\nIn-person and on Zoom (Passcode: 680207)",
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        "event_title":"ClariTEA: Informal Undergraduate Advising Event",
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        "event_title":"Robotics Winter Demo Days",
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        "datetime_modified":"20260505T152141",
        "datetime_start":"20260512T130000",
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        "event_title":"Multimodal Fusion and Temporal Reasoning for Intelligent Robot Perception",
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        "combined_title":"Multimodal Fusion and Temporal Reasoning for Intelligent Robot Perception: PhD Defense, Jingyu Song",
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        "description":"Committee chair: Katie Skinner\n\nAbstract:\nReliable autonomy for field robots depends on perception systems that can operate under difficult sensing conditions. In real-world environments, robot perception is often degraded by low-texture visual patterns, environmental disturbances, adverse weather, occlusions, and sensor failures. This dissertation develops multimodal fusion and temporal reasoning methods that improve the robustness, scalability, and accuracy of robot perception across challenging environments.\n\nThe first part of this thesis addresses state estimation and dense mapping for underwater robots, where wave disturbance and low-texture environments often cause vision-based localization to fail. We introduce TURTLMap, a real-time localization and dense mapping framework for low-cost underwater robots. TURTLMap fuses Doppler velocity log, inertial, and pressure measurements for robust localization, while using stereo depth to construct dense 3D maps. Real-world experiments in a water tank environment, evaluated with underwater motion capture and a reference 3D structure, demonstrate accurate robot tracking and mapping under low-texture and wave-disturbed conditions.\n\nThe second part studies adaptive multimodal fusion for autonomous vehicle perception. We introduce LiRaFusion, a LiDAR-radar fusion network that combines joint feature encoding with adaptive feature weighting to better exploit the complementary strengths of LiDAR and radar. Experiments on large-scale 3D object detection benchmarks show that this design improves detection performance over existing fusion methods. Building on this direction, we develop CRKD, a cross-modality knowledge distillation framework that transfers knowledge from a high-performing LiDAR-camera teacher to a scalable camera-radar student. This approach provides a practical pathway for using high-quality sensor data from test fleets to improve cost-effective sensing configurations for consumer vehicles, achieving state-of-the-art camera-radar object detection performance.\n\nThe third part explores temporal reasoning for road scene understanding. We introduce MemFusionMap, a memory-based framework for online vectorized HD map construction that improves temporal fusion by combining current BEV features with multiple working-memory features. MemFusionMap further maintains a temporal overlap heatmap, which provides a spatiotemporal cue for how historical observations overlap with the current field of view and helps the model reason over memory more adaptively. Together, these designs improve map construction under challenging and complex road conditions, including occlusion and dynamic scene changes, while preserving efficient runtime and compatibility with multiple perception models.\n\nFinally, this thesis develops CRISP, a spatiotemporal camera-radar pretraining framework for autonomous driving. CRISP learns transferable bird\u2019s-eye-view representations by forecasting future LiDAR point clouds from historical camera and radar observations, using LiDAR as privileged supervision only during pretraining. At deployment, the model operates using camera-radar inputs alone. Experiments on real-world benchmarks show that CRISP improves long-horizon point cloud forecasting and transfers effectively to downstream tasks including 3D object detection, tracking, online mapping, motion forecasting, future occupancy prediction, and planning.\n\nTogether, these contributions show how multimodal sensing, cross-modality knowledge transfer, temporal memory, and predictive pretraining can make robot perception more reliable under practical sensing constraints. The resulting methods improve localization, mapping, perception, prediction, and planning across challenging underwater and autonomous driving environments.",
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        "description":"Committee chair: \nProfessor Kira Barton\n\nIn FMCRB 2300 and on Zoom.\n\nAbstract: \nToday's microelectronics are built in billion-dollar fabrication facilities using processes that are fast but inflexible. What if we could instead print electronic circuits the way an inkjet printer puts ink on paper, but at a scale hundreds of times smaller than a human hair? This vision has driven growing interest in additive manufacturing approaches that can integrate heterogeneous components into functional circuits without the constraints of conventional lithographic fabrication. However, reliable high-resolution conductive printing, automated circuit assembly at the microscale, and real-time process monitoring remain largely unresolved challenges. This dissertation develops an integrated framework toward autonomous micro\/nanoscale hybrid electronics packaging using electrohydrodynamic jet (e-jet) printing, organized around three progressive thrusts.\n\nThe first thrust establishes a characterization and design framework for submicron printed metal nanoparticle interconnects. The three-stage fabrication process from droplet generation, multilayer line formation, to thermal sintering is systematically investigated for silver and gold nanoparticle inks. Quantitative models linking process parameters to printed feature dimensions enable reliable submicron patterning, achieving conductive gold interconnects with line widths down to 300 nm. Integration with micromodular transistors validates the framework at the device level, with printed interconnects achieving performance comparable to lithography-defined wiring.\n\nThe second thrust develops an automated perception\u2013planning\u2013printing pipeline for adaptive circuit routing. Vision-based component detection and algorithmic route planning enable fully autonomous wiring of randomly placed micro-devices. The framework is validated through fabrication of functional inverter circuits and has been extended to memristive device integration and paper-based flexible substrates, confirming its applicability across heterogeneous device types and material systems.\n\nThe third thrust introduces optical density as a real-time volumetric sensing modality for micro-additive manufacturing. Quantitative correlations between in-situ optical measurements, physical line geometry, and electrical resistivity establish a direct inference chain from real-time sensing to functional performance. This sensing framework is integrated into a closed-loop control architecture for automated line width regulation.\n\nTogether, these contributions connect process science, manufacturing automation, and in-situ sensing into a cohesive framework for autonomous micro\/nanoscale electronics fabrication, advancing printed hybrid microelectronics toward greater functionality, reliability, and scalability.",
        "occurrence_notes":"and on Zoom",
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        "building_name":"Ford Robotics Building",
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        "room":"2300",
        "location_name":"Ford Robotics Building",
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        "tags":["Robotics"],
        "website":"https:\/\/umich.zoom.us\/j\/92530477135",
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