I am interested in problems at the intersection of optimization, machine learning and robotics design. I study the interaction of data-driven Learning for autonomy and Design for automation for human skill-augmentation and decision support.
I work on enabling autonomous systems to learn from imprecise information for performing a range of tasks with independence and flexibility. My work employs and contributes to techniques in non-convex, discrete optimization, design and deep representation learning.
My Ph.D. focuses on autonomy in two healthcare applications:
Learning & Automation in Surgical Subtasks
Robot Assisted Minimally Invasive Surgery (RMIS) was used in manual teleoperation mode in over 570,000 procedures worldwide in 2014 with 3000 Da Vinci systems. However, RMIS procedures are tedious and depend highly on surgeon skill. Autonomy of surgical subtasks has the potential to assist surgeons, reduce fatigue, and enhance manual telesurgery. Moreover, the growing corpus of surgical data can enable data-driven learning for automation. I research learning from expert demonstrations in surgery with unique challenges such as specular workspace, constrained dexterity, and highly noisy datasets.
- TSC-DL: Segmentation with Deep Learning
A new unsupervised algorithm that leverages video & kinematic data for task-level segmentation using pretrained CNNs to identify spatio-temporal task segmentation.
[ICRA16-under review] [Tutorial-Video]
- Autonomous Multi-Throw Suturing
We present an optimization framework and a novel mechanical needle guide design to perform supervised automation of multi-throw suturing.
[ICRA16-under review] [Suturing-Video]
- Autonomous Tumor Localization & Resection
We present two designs for surgical automation: a low-cost end-effector mount and a fluid injection system. We automate a 4-step tumor resection procedure to locate and debride a subcutaneous tumor.
[ICRA16-under review] [Video] Best Video Award
- TSC: Unsupervised Task Segmentation
We proposed an unsupervised algorithm for recovering structure from demonstration data and autonomously perform semantic segmentation.
- Disposable Sensors for Minimally Invasive Surgery
We proposed a Disposable Haptic Palpation Probe for Locating Subcutaneous Blood Vessels in Robot-Assisted Minimally Invasive Surgery.
[CASE15] Best Poster/Demo Award.
- Learning by Observation for Surgical Subtasks
We proposed a Learning by Observation algorithm for surgical subtasks demosttrated with multilateral Cutting of 3D Viscoelastic and 2D Orthotropic Tissue Phantoms.
[ICRA15] [Video] [Short Talk]
Best Medical Robotics Paper Award Finalist
Radiation Therapy for Cancer: Planning and Delivery
High Dose Rate Brachytherapy (HDR-BT) is an internal radiation therapy and is used for over 500,000 cancer patients annually in the US. It is prevalent for treatment in many body sites such as mouth, breast and prostate. It involves radioactive sources placed temporarily proximal to or within tumors.
Current methods for intracavitary and interstitial HDR-BT use generic templates which result in inadequate dose coverage and healthy organ puncture, respectively.
We present novel patient specific 3D-printed implants and needle guides for respective modes; we also evaluate robot-assisted needle implants for interstitial HDR-BT.
- 3D Printed Implants for Intracavitary Brachytherapy
We propose a new approach that builds on progress in 3D printing and steerable needle motion planning to create customized implants containing customized curvature-constrained internal channels that fit securely, minimize air gaps, and precisely guide radioactive sources through printed channels.
[CASE13] [Short Talk] [Slides]
- Material Evaluation of 3D Printed GYN Implants
The study evaluates the radiation attenuation properties of PC-ISO, a commercially available, biocompatible, sterilizable 3D printing material, and its suitability for customized, single-use gynecologic (GYN) brachytherapy applicators that have the potential for accurate guiding of seeds through linear and curved internal channels.
- Robot-Guided Needle Insertion for HDR-BT
We leverage human-centered automation to reduce side effects from HDR-BT in prostate cancer by efficiently delivering radiation to the prostate while minimizing trauma to sensitive structures such as the penile bulb. We modify the Acubot-RND system to guide needles into desired skew-line arrangements algorithmically calculated with needle planning and inverse dose planning algorithms.
[CASE12] [T-ASE13] [Video] [CASE-Talk]
Best Application Paper Award.
- Reachability Analysis for Needle Planning in HDR-BT
We propose a new approach that builds on recent results in 3D printing and steerable needle motion planning to create customized implants containing customized curvature-constrained internal channels that fit securely, minimize air gaps, and precisely
guide radioactive sources through printed channels.
- 3D Printed Guides for Prostate Brachytherapy
We propose the use of patient specific custom needle guides for needle configuration implant in Prostate HDR-BT. This work builds upon the robot-guided needle implants, and attempts to evaluate a low-cost yet effective method for achieving clinical objectives.