Students are nominated for the award by the faculty based on numerous factors, including peer-reviewed publications, awards received for presentations or publications, potential contribution to the field and society, leadership in a research group or community, participation in professional societies, and overall academic achievement.

She was recognized at the 2018 College of Engineering Graduate Student Research, Teaching, and Service Awards Luncheon on April 24th.

Rojas gave a talk regarding the current state of the System for Telementoring with Augmented Reality (STAR). The talk included a comparison between two different STAR versions: a Tablet-based and a Head-Mounted Display.

A poster summarizing the talk can be found here.

Zhou, T., Cha, J. S., Gonzalez, G. T., Wachs, J. P., Sundaram, C., & Yu, D. (2018, March). Joint Surgeon Attributes Estimation in Robot-Assisted Surgery. In Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 285-286). ACM.

Proceeding
Poster

Cabrera, M. E., Voyles, R. M., & Wachs, J. P. (2018, March). Coherence in One-Shot Gesture Recognition for Human-Robot Interaction. In Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 75-76). ACM.

Proceeding
Poster

Sanchez-Tamayo, N., & Wachs, J. P. (2018, March). Collaborative Robots in Surgical Research: a Low-Cost Adaptation. In Companion of the 2018 ACM/IEEE International Conference on Human-Robot Interaction (pp. 231-232). ACM.

Proceeding
Poster

Brief Description: Humans tend to create the gestures on the fly and conventional machine learning systems lack adaptability to learn new gestures beyond the training stage. This problem can be best addressed using Zero Shot Learning (ZSL), a paradigm in machine learning that aims to recognize unseen objects by just having a description of them. ZSL for gestures has hardly been addressed in computer vision research due to the inherent ambiguity and the contextual dependency associated with the gestures. This work proposes an approach for Zero Shot Gestural Learning (ZSGL) by leveraging the semantic information that is embedded in the gestures.

Maria Eugenia Cabrera, IE PhD student, has received the best three minute presentation award at the recent AAAI (Association for the Advancement of Artificial Intelligence) Conference.

With the help of Dr. Juan Wachs, Maria is studying the possibility of switching from joystick interfaces to touchless ones to control the robots used in Robotic Assisted Surgery (RAS).  With touchless interfaces, the tasks and movements of the robots can be controlled by free hand gestures. This type of interface is more   natural to the surgeons since they can make movements exactly the same as if they were operating using their bare hands, and the robot can mimic and transform those instructions to actions with precision and efficiency.

The study presented took advantage of different gaming technologies currently in the market which cover both touch-based and touchless interfaces (among which are Kinect, Leap Motion, Hydra Razer, Omega 7 and the traditional keyboard) to compare the performance of users when they were carrying out two different tasks related to surgery. After comparing the results using several metrics, it was seen that touchless interfaces show potential and with further work, they could be incorporated to the medical field.

Cabrera’s paper, “Touchless telerobotic surgery–is it possible at all?” was co-authored by Industrial Engineering PhD student Tian Zhou and Professor Juan Wachs.