Isabella Huang
Papers
10
Total Citations
126
H-Index
7
About
Isabella Huang is a robotics researcher whose work sits at the intersection of robotic manipulation, soft robotics, and tactile sensing. Her research focuses on enabling robots to reliably grasp and interact with deformable objects — a challenge central to applications in food processing, robotic surgery, and assistive robotics. Her most influential contribution, the **DefGraspSim** series (2021–2023, accumulating over 55 citations), introduced physics-based simulation frameworks for predicting grasp outcomes on 3D deformable objects, culminating in **DefGraspNets**, which leverages graph neural networks to reason over complex deformation and stress fields. Complementing this, her **IPC-GraspSim** work (2022, 13 citations) addresses the persistent sim-to-real gap in parallel-jaw grasping using incremental potential contact models. Huang has also made notable strides in soft tactile sensing, developing depth camera-based fingertip devices and high-resolution tactile interfaces for physical human-robot interaction (cited 23 and 11 times, respectively). Her work on tactile contour following and robot learning from demonstration further demonstrates her commitment to safe, capable robots in unstructured environments. Taken together, Huang's research builds a coherent vision of robots that can sense, simulate, and adapt their physical interactions with the world.
Research Focus
Key Achievements
Top Papers
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- 3DefGraspSim: Simulation-based grasping of 3D deformable objects14 citations · 2021
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- 5High Resolution Soft Tactile Interface for Physical Human-Robot Interaction11 citations · 2020
- 6Soft Tactile Contour Following for Robot-Assisted Wiping and Bathing10 citations · 2022
- 7DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets7 citations · 2023
- 8Nonverbal Robot Feedback for Human Teachers7 citations · 2019
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