About

Lilly Chin is an assistant professor at the University of Texas at Austin in the Electrical and Computer Engineering department. She leads the MERGe Lab, where she focuses on her current research projects. Her personal website emphasizes her identity beyond her academic role, showcasing her philosophy towards research and the influences that have shaped it. In addition to her academic work, she archives a variety of personal projects that span engineering, visual art, and both amateur and professional writing. Notably, she also highlights her higher visibility activities, including her achievement as the 2017 College Champion on Jeopardy! and her reputation as a "spiciest memelord."

Research topics

• Artificial Intelligence
• Computer Science
• Engineering
• Computer vision
• Mechanical engineering
• Materials science
• Nanotechnology
• Human–computer interaction
• Control engineering
• Embedded system
• Systems engineering
• Aerospace engineering

Selected publications

• High-Fidelity, Customizable Force Sensing for the Wearable Human-Robot Interface

  arXiv (Cornell University) · 2026-02-13

  articleOpen accessSenior author

  Mechanically characterizing the human-machine interface is essential to understanding user behavior and optimizing wearable robot performance. This interface has been challenging to sensorize due to manufacturing complexity and non-linear sensor responses. Here, we measure human limb-device interaction via fluidic innervation, creating a 3D-printed silicone pad with embedded air channels to measure forces. As forces are applied to the pad, the air channels compress, resulting in a pressure change measurable by off-the-shelf pressure transducers. We demonstrate in benchtop testing that pad pressure is highly linearly related to applied force ($R^2 = 0.998$). This is confirmed with clinical dynamometer correlations with isometric knee torque, where above-knee pressure was highly correlated with flexion torque ($R^2 = 0.95$), while below-knee pressure was highly correlated with extension torque ($R^2 = 0.75$). We build on these idealized settings to test pad performance in more unconstrained settings. We place the pad over \textit{biceps brachii} during cyclic curls and stepwise isometric holds, observing a correlation between pressure and elbow angle. Finally, we integrated the sensor into the strap of a lower-extremity robotic exoskeleton and recorded pad pressure during repeated squats with the device unpowered. Pad pressure tracked squat phase and overall task dynamics consistently. Overall, our preliminary results suggest fluidic innervation is a readily customizable sensing modality with high signal-to-noise ratio and temporal resolution for capturing human-machine mechanical interaction. In the long-term, this modality may provide an alternative real-time sensing input to control / optimize wearable robotic systems and to capture user function during device use.

  PublisherOA PDF

• Exp-Force: Experience-Conditioned Pre-Grasp Force Selection with Vision-Language Models

  ArXiv.org · 2026-03-09

  articleOpen accessSenior author

  Accurate pre-contact grasp force selection is critical for safe and reliable robotic manipulation. Adaptive controllers regulate force after contact but still require a reasonable initial estimate. Starting a grasp with too little force requires reactive adjustment, while starting a grasp with too high a force risks damaging fragile objects. This trade-off is particularly challenging for compliant grippers, whose contact mechanics are difficult to model analytically. We propose Exp-Force, an experience-conditioned framework that predicts the minimum feasible grasping force from a single RGB image. The method retrieves a small set of relevant prior grasping experiences and conditions a vision-language model on these examples for in-context inference, without analytic contact models or manually designed heuristics. On 129 object instances, ExpForce achieves a best-case MAE of 0.43 N, reducing error by 72% over zero-shot inference. In real-world tests on 30 unseen objects, it improves appropriate force selection rate from 63% to 87%. These results demonstrate that Exp-Force enables reliable and generalizable pre-grasp force selection by leveraging prior interaction experiences. http://expforcesubmission.github.io/Exp-Force-Website/

  PublisherOA PDF

• Exp-Force: Experience-Conditioned Pre-Grasp Force Selection with Vision-Language Models

  Open MIND · 2026-03-09

  preprintSenior author

  Accurate pre-contact grasp force selection is critical for safe and reliable robotic manipulation. Adaptive controllers regulate force after contact but still require a reasonable initial estimate. Starting a grasp with too little force requires reactive adjustment, while starting a grasp with too high a force risks damaging fragile objects. This trade-off is particularly challenging for compliant grippers, whose contact mechanics are difficult to model analytically. We propose Exp-Force, an experience-conditioned framework that predicts the minimum feasible grasping force from a single RGB image. The method retrieves a small set of relevant prior grasping experiences and conditions a vision-language model on these examples for in-context inference, without analytic contact models or manually designed heuristics. On 129 object instances, ExpForce achieves a best-case MAE of 0.43 N, reducing error by 72% over zero-shot inference. In real-world tests on 30 unseen objects, it improves appropriate force selection rate from 63% to 87%. These results demonstrate that Exp-Force enables reliable and generalizable pre-grasp force selection by leveraging prior interaction experiences. http://expforcesubmission.github.io/Exp-Force-Website/

  DOI

• High-Fidelity, Customizable Force Sensing for the Wearable Human-Robot Interface

  Open MIND · 2026-02-13

  preprintSenior author

  Mechanically characterizing the human-machine interface is essential to understanding user behavior and optimizing wearable robot performance. This interface has been challenging to sensorize due to manufacturing complexity and non-linear sensor responses. Here, we measure human limb-device interaction via fluidic innervation, creating a 3D-printed silicone pad with embedded air channels to measure forces. As forces are applied to the pad, the air channels compress, resulting in a pressure change measurable by off-the-shelf pressure transducers. We demonstrate in benchtop testing that pad pressure is highly linearly related to applied force ($R^2 = 0.998$). This is confirmed with clinical dynamometer correlations with isometric knee torque, where above-knee pressure was highly correlated with flexion torque ($R^2 = 0.95$), while below-knee pressure was highly correlated with extension torque ($R^2 = 0.75$). We build on these idealized settings to test pad performance in more unconstrained settings. We place the pad over \textit{biceps brachii} during cyclic curls and stepwise isometric holds, observing a correlation between pressure and elbow angle. Finally, we integrated the sensor into the strap of a lower-extremity robotic exoskeleton and recorded pad pressure during repeated squats with the device unpowered. Pad pressure tracked squat phase and overall task dynamics consistently. Overall, our preliminary results suggest fluidic innervation is a readily customizable sensing modality with high signal-to-noise ratio and temporal resolution for capturing human-machine mechanical interaction. In the long-term, this modality may provide an alternative real-time sensing input to control / optimize wearable robotic systems and to capture user function during device use.

  DOI

• High-Fidelity, Customizable Force Sensing for the Wearable Human-Robot Interface.

  PubMed · 2026-02-13

  articleSenior author

  Mechanically characterizing the human-machine interface is essential to understanding user behavior and optimizing wearable robot performance. This interface has been challenging to sensorize due to manufacturing complexity and non-linear sensor responses. Here, we measure human limb-device interaction via fluidic innervation, creating a 3D-printed silicone pad with embedded air channels to measure forces. As forces are applied to the pad, the air channels compress, resulting in a pressure change measurable by off-the-shelf pressure transducers. We demonstrate in benchtop testing that pad pressure is highly linearly related to applied force ($R^2 = 0.998$). This is confirmed with clinical dynamometer correlations with isometric knee torque, where above-knee pressure was highly correlated with flexion torque ($R^2 = 0.95$), while below-knee pressure was highly correlated with extension torque ($R^2 = 0.75$). We build on these idealized settings to test pad performance in more unconstrained settings. We place the pad over \textit{biceps brachii} during cyclic curls and stepwise isometric holds, observing a correlation between pressure and elbow angle. Finally, we integrated the sensor into the strap of a lower-extremity robotic exoskeleton and recorded pad pressure during repeated squats with the device unpowered. Pad pressure tracked squat phase and overall task dynamics consistently. Overall, our preliminary results suggest fluidic innervation is a readily customizable sensing modality with high signal-to-noise ratio and temporal resolution for capturing human-machine mechanical interaction. In the long-term, this modality may provide an alternative real-time sensing input to control / optimize wearable robotic systems and to capture user function during device use.

  Publisher

• FORTE: Tactile Force and Slip Sensing on Compliant Fingers for Delicate Manipulation

  IEEE Robotics and Automation Letters · 2026-02-09

  articleOpen accessSenior author

  Handling fragile objects remains a major challenge for robotic manipulation. Tactile sensing and soft robotics can improve delicate object handling, but typically involve high integration complexity or slow response times. We address these issues through FORTE, an easy-to-fabricate tactile sensing system comprised of 3D-printed fin-ray grippers with internal air channels. FORTE provides low-latency force and slip feedback, enabling us to apply just enough force to grasp objects without damaging them. We accurately estimate grasping forces from 0–8 N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.2 N, and detect slip events within 100 ms of occurring. FORTE can grasp a wide range of slippery, fragile, and deformable objects, including raspberries and potato chips with 92% success and achieves 93% accuracy in detecting slip events. These results highlight FORTE's potential as a robust solution for delicate manipulation. <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://merge-lab.github.io/FORTE/</uri>

  PublisherOA PDFDOI

• BiFlex: A Passive Bimodal Stiffness Flexible Wrist for Manipulation in Unstructured Environments

  IEEE Robotics and Automation Letters · 2025-09-02 · 1 citations

  articleOpen access

  Robotic manipulation in unstructured, human-centric environments poses a dual challenge: achieving the precision need for delicate free-space operation while ensuring safety during unexpected contact events. Traditional wrists struggle to balance these demands, often relying on complex control schemes or complicated mechanical designs to mitigate potential damage from force overload. In response, we present BiFlex, a flexible robotic wrist that uses a soft buckling honeycomb structure to provide a natural bimodal stiffness response. The higher stiffness mode enables precise household object manipulation, while the lower stiffness mode provides the compliance needed to adapt to external forces. We design BiFlex to maintain a fingertip deflection of less than 1 cm while supporting loads up to 500 g and create a BiFlex wrist for many grippers, including Panda, Robotiq, and BaRiFlex. We validate BiFlex under several real-world experimental evaluations, including surface wiping, precise pick-and-place, and grasping under environmental constraints. We demonstrate that BiFlex simplifies control while maintaining precise object manipulation and enhanced safety in real-world applications. More information and videos are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://robin-lab.cs.utexas.edu/BiFlex/</uri>

  PublisherOA PDFDOI

• Large-Expansion Bi-Layer Auxetics Create Compliant Cellular Motion

  2025-05-19

  article1st authorCorresponding

  There is significant interest in creating compliant modular robots that can change their volume. Inspired by how biological cells move, these systems can potentially combine the resilience of modular robotics with the increased environmental interactions of soft robotics. However, current versions have limited speed, expansion, and portability. In this paper, we address these concerns through AuxSwarm, a compliant system composed of auxetic-based robotic voxels. These voxels control their volume through a scissor-like bi-layer auxetic design, growing up to 1.57 times their original size in 0.2 seconds. This combination of speed and expansion is unique across modular soft robots, enabling dynamic locomotion capabilities. We characterize the voxels and demonstrate the versatility of this approach through case studies of 2D bending and 3D cube flipping. AuxSwarm provides a first step towards addressable voxel-based smart materials, while simultaneously addressing the robustness and actuation challenges faced by soft robots.

  PublisherDOI

• BiFlex: A Passive Bimodal Stiffness Flexible Wrist for Manipulation in Unstructured Environments

  arXiv (Cornell University) · 2025-04-11

  preprintOpen access

  Robotic manipulation in unstructured, humancentric environments poses a dual challenge: achieving the precision need for delicate free-space operation while ensuring safety during unexpected contact events. Traditional wrists struggle to balance these demands, often relying on complex control schemes or complicated mechanical designs to mitigate potential damage from force overload. In response, we present BiFlex, a flexible robotic wrist that uses a soft buckling honeycomb structure to provides a natural bimodal stiffness response. The higher stiffness mode enables precise household object manipulation, while the lower stiffness mode provides the compliance needed to adapt to external forces. We design BiFlex to maintain a fingertip deflection of less than 1 cm while supporting loads up to 500g and create a BiFlex wrist for many grippers, including Panda, Robotiq, and BaRiFlex. We validate BiFlex under several real-world experimental evaluations, including surface wiping, precise pick-and-place, and grasping under environmental constraints. We demonstrate that BiFlex simplifies control while maintaining precise object manipulation and enhanced safety in real-world applications.

  PublisherDOI

• Embedded air channels transform soft lattices into sensorized grippers

  2024-05-13 · 1 citations

  article

  Sensing plays a pivotal role in robotic manipulation, dictating the accuracy and versatility with which objects are handled. Vision-based sensing methods often suffer from fabrication complexity and low durability, while approaches that rely on direct measurements on the gripper often have limited resolution and are difficult to scale. Here, we present a soft robotic gripper made out of two cubic lattices that are sensorized by embedding air channels within the structure. The lattices are 3D printed from a single build material, simplifying the fabrication process. The flexibility of this approach offers significant control over sensor and lattice design, while the pressure-based internal sensing provides measurements with minimal disruption to the grasping surface. With only 12 sensors, 6 per lattice, this gripper can estimate an object’s weight and location and offer new insights into grasp parameters like friction coefficients and grasp force.

  PublisherDOI

Frequent coauthors

• Daniela Rus

  19 shared

• Ryan L. Truby

  Northwestern University

  7 shared

• Jeffrey I. Lipton

  United States Army Combat Capabilities Development Command

  6 shared

• Annan Zhang

  Massachusetts Institute of Technology

  6 shared

• Gregory Xie

  Massachusetts Institute of Technology

  3 shared

• Matthieu Kirchmeyer

  3 shared

• Jeana Choi

  Massachusetts Institute of Technology

  3 shared

• Rachel Holladay

  Massachusetts Institute of Technology

  2 shared

Labs

• MERGe LabPI

  Making Materials Embody Robots, Geometrically

Education

• Doctor of Philosophy, Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2023

• Bachelor of Science, Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2017

Awards & honors

• Fellow of the Jack Kilby/Texas Instruments Endowed Faculty F…
• NSF Graduate Research Fellowship
• Hertz Foundation Graduate Fellowship
• Paul and Daisy Soros Fellowship for New Americans
• 2019 IEEE Robosoft Best Poster Award