Current Projects
Soft Robotics
We develop novel compliant mechanisms and robotic systems that leverage embodied intelligence to perform tasks that are inaccessible to traditional rigid robots.
Computer Vision for Biomedical Applications
Our lab develops and deploys vision algorithms for detecting environmental anomalies and biological behaviors via machine learning techniques.
Robot Hands, Grasping and Tactile Sensing
Our research focuses on addressing common problems in robot hands through the use of mechanical intelligence, such as compliant joints, numerical optimization to reduce the complexity of hand actuation, and the side-by-side development of low-cost, robust sensors and perception and grasping algorithms to enable autonomous operation.
Ultrasound Imaging
Our lab develops advanced ultrasound imaging technologies to enhance the visualization, tracking, and analysis of dynamic biological structures. We focus on improving image quality, expanding field-of-view, and enabling real-time interpretation for clinical and research applications. By combining innovations in image processing, computer vision, and biomedical engineering, our work supports applications in surgical planning, robotic assistance, and personalized medicine.
Rehabilitation Robotics
Stroke and post-operative upper-limb injury patients require continuous rehabilitation to regain normal motor function. Our research focuses on developing cable-driven, wearable rehabilitation devices that can help doctors monitor patient progress and provide robot-assisted physical therapy both in the clinic and at home.
Past Projects
Biomechanical Modeling and Surgical Planning
Heart valve repair is technically difficult; providing the surgeon with an anatomically and biomechanically accurate computer model of a particular patient’s valve could enable preoperative surgical planning and potentially improve surgical outcome. We have previously developed methods for automatically segmenting heart valve structures in real-time 3d ultrasound images, and we can simulate the ability of valves to close properly using fast & biomechanically accurate computational modeling methods.
Robotic Surgery
To perform procedures inside a patient’s heart (intracardiac surgery), cardiopulmonary bypass is necessary so the surgeon can work on a relaxed, open heart. Although this technique is the current standard, studies have identified numerous adverse effects of cardiopulmonary bypass. Minimally invasive procedures could eliminate the need for a cardiopulmonary bypass, thereby allowing the surgeon to work directly inside the beating heart. Real-time 3D ultrasound-guided minimally invasive robotic surgery has the potential to enable beating heart intracardiac surgery.
Image Processing and Medical Imaging
Our research harnesses advanced imaging technologies and computational techniques to deepen our understanding of the human body in motion. We focus on developing innovative methods for acquiring, processing, and interpreting medical images to visualize and quantify the dynamics of muscles, soft tissues, and surgical instruments in real time. By integrating ultrasound imaging with computer vision, signal processing, and machine learning, we build tools for clinical diagnostics, human-machine interfaces, and surgical guidance.
Teleoperated Robots
Our research in teleoperated robotics focuses on enhancing precision, feedback, and autonomy in human-robot interaction for applications in complex and remote environments. We investigate how joint and link flexibility, common in lightweight or cable-driven robotic systems, affects control transparency, force feedback, and task performance. By integrating advanced sensing at the robot’s tip and developing novel feedback strategies, we improve both position tracking and tactile perception, enabling operators to better distinguish subtle mechanical cues such as tissue stiffness. In parallel, we are advancing machine-assisted teleoperation through real-time world modeling, using sensor-driven insights to assist users with quantitative data like object geometry and alignment during remote manipulation. These tools aim to augment human capabilities in critical domains such as surgical intervention, space operations, and remote assembly.
Haptic Interfaces
Our lab explores cutting-edge haptic technologies to bridge the gap between human touch and digital interaction. By developing high-fidelity tactile and force-feedback systems, we aim to enhance precision and realism in tasks ranging from remote surgery to virtual assembly. Our work includes studying how latency and signal fidelity affect human performance in haptic environments using magnetic levitation interfaces, as well as designing tactile shape displays that convey subtle pressure patterns from inside the body to a surgeon’s fingertips. Through these innovations, we seek to create more intuitive, responsive, and immersive touch-based interfaces for medical and engineering applications.
Tactile Sensing and Display
Tactile sensing and display technologies enable the restoration and enhancement of the sense of touch in applications ranging from minimally invasive surgery to virtual environments. We develop advanced tactile sensors and feedback systems that allow users to feel physical interactions through robotic instruments or digital simulations. These systems measure complex pressure distributions, model tissue contact mechanics, and recreate tactile sensations at the user’s fingertips through shape displays and vibrotactile feedback. Our designs incorporate high-resolution actuator arrays, real-time signal processing, and biomechanical modeling to simulate realistic touch cues such as lump detection, surface stiffness, and fine texture. By combining insights from neuroscience, robotics, materials science, and human perception, we are advancing technologies that make touch a meaningful and precise communication channel in both medical and virtual domains.
Tissue Mechanics
Our research in tissue mechanics seeks to understand and model how soft biological tissues deform under physical stress, with applications in surgical simulation, robotic intervention, and pathology assessment. We use a combination of experimental testing, advanced imaging (such as 3D ultrasound), and finite element modeling to capture the complex, nonlinear, and time-dependent behavior of organs like the liver, kidney, and vocal folds. From developing perfused ex vivo testing systems and constitutive models of poro-viscoelastic tissue behavior, to measuring the in vivo stress response of vocal tissues and simulating myocardial injury during surgery, our work provides critical insights into tissue-level biomechanics. These models and data not only enhance realism in medical simulators but also inform safer, more precise surgical techniques and treatments. By establishing experimental standards and parameter estimation frameworks, we aim to create robust, validated models that serve as a foundation for future innovations in computational medicine and bioengineering.
Biological Motor Control
Understanding how humans generate, regulate, and adapt movement is central to advancing both clinical rehabilitation and robotic design. Our work investigates the neural and biomechanical principles of motor control through studies of hand impedance, task learning, and neuromotor recovery. We explore how humans modulate limb stiffness in response to task demands, enabling dexterous actions like drumming or precision grasping. Using tools such as haptic interfaces and instrumented manipulanda, we measure and model force-motion dynamics during interaction. Our research also examines motor adaptation over time, revealing how learning shapes mechanical behavior. In parallel, we study functional brain imaging and physiological feedback in stroke recovery, leveraging techniques like hypnosis and diffuse optical tomography (DOT) to understand and promote cortical reorganization. By connecting brain, behavior, and biomechanics, we aim to inform therapeutic interventions, motor learning strategies, and bioinspired robotic systems.
Impedance Control
Our research in impedance control focuses on enabling robots to interact with the physical world in a safe, adaptive, and human-like manner. Using high-speed control hardware and force-torque sensing, we investigate methods for tactile exploration, object shape reconstruction, and compliance-based interaction. This approach allows robots to infer the mechanical properties of objects and adjust their grasp and manipulation strategies accordingly. Inspired by the human arm’s ability to modulate stiffness during physical tasks, our work advances the development of intelligent, force-aware robotic systems for delicate manipulation, object recognition, and responsive human-robot interaction.