Masoud Moghani

I am a PhD student at the University of Toronto, advised by Animesh Garg working at the intersection of robotics, machine learning, and simulation. My research focuses on robot learning for manipulation and deformable object simulation. I am also working on autonomy in surgical robotics.

Email / Google Scholar / Twitter / YouTube / Github

Publications

	SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants Masoud Moghani, Lars Doorenbos, William Chung-Ho Panitch, Sean Huver, Mahdi Azizian, Ken Goldberg, Animesh Garg (Under review), 2024 website / video SuFIA, a framework for natural language-guided augmented dexterity for robotic surgical assistants.
	ORBIT-Surgical: An Open Simulation Framework for Accelerated Learning in Surgical Autonomy Qinxi Yu^, Masoud Moghani^, Karthik Dharmarajan, Vincent Schorp, William Chung-Ho Panitch, Jingzhou Liu, Kush Hari, Huang Huang, Mayank Mittal, Ken Goldberg, Animesh Garg ^Equal Contribution IEEE International Conference on Robotics and Automation (ICRA)*, 2024 website / video / paper / pdf ORBIT-Surgical, a physics-based surgical robot simulation framework with photorealistic rendering.
	Robot-Assisted Vascular Shunt Insertion with the dVRK Surgical Robot Karthik Dharmarajan, Will Panitch, Baiyu Shi, Huang Huang, Lawrence Yunliang Chen, Masoud Moghani, Qinxi Yu, Kush Hari, Thomas Low, Danyal Fer, Animesh Garg, Ken Goldberg Journal of Medical Robotics Research (JMRR), 2023 website / paper A trimodal framework for vascular shunt insertion assisted by a da Vinci Research Kit (dVRK) robotic surgical assistant.
	A Small Steerable Tip Based on Dielectric Elastomer Actuators Siyoung Lee, Masoud Moghani, Ang Li, Mihai Duduta IEEE Robotics and Automation Letters (RA-L), 2023 paper A soft robotic approach for a steerable tip capable of guiding a small flexible insertion tube.
	A Lightweight Magnetorheological Actuator Using Hybrid Magnetization Masoud Moghani, Mehrdad R. Kermani IEEE/ASME Transactions on Mechatronics (T-Mech), 2019 paper / pdf The design and validation of a high-performance compliant actuator for robotic manipulators.
	Hysteresis modeling of a hybrid magneto-rheological actuator Masoud Moghani, Mehrdad R. Kermani IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2016 paper A neural network based closed-loop torque control strategy for a compliant actuator.
	Design and Development of a Hybrid Magneto-Rheological Clutch for Safe Robotic Applications Masoud Moghani, Mehrdad R. Kermani IEEE International Conference on Robotics and Automation (ICRA), 2016 paper A novel compliant actuator for safe robotic applications.

Teaching

CSC413H1-2516H - Neural Networks and Deep Learning (TA)
MIE404H1 - Control Systems I (Co-lecturer, 2x - Head TA)
ECE470H1 - Robot Modeling and Control (2x - TA)
MIE505H1 - Micro/Nano Robotics (TA)
ECE 4460B - Real Time Embedded Systems (2x - TA)

Source code from Jon Barron website's source code.