Danyal Saqib

I am a PhD student at the University of British Columbia (UBC) in Vancouver, Canada, researching data-driven control, modeling, and analysis of complex systems with applications to robotics and embodied physical intelligence. I am a part of the Padoan Lab within the Department of Electrical and Computer Engineering, supervised by Dr. Alberto Padoan.

I previously completed my MSc at the University of Calgary, with research focusing on Robotics and Controls as part of the Calgary Human-Robot Collaboration (CHRC) Lab, supervised by Dr. Marie Charbonneau. I received my Bachelor's degree in Electrical Engineering from SEECS, NUST. My undergraduate thesis was supervised by Dr. Wajahat Hussain, in the domain of self-supervised learning for robotic systems.

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Research

My general research interests include control theory, learning, and robotics. I am currently researching the modeling, analysis, and control of interconnected robotic systems, and how physical reservoir systems lend themselves to complex tasks.

I am privileged to have had the opportunity to work with and design controllers for several interesting robots, such as the Reachy Robot by Pollen Robotics at the University of Calgary's CHRC Lab, and the Talos Humanoid Robot by PAL Robotics at the University of Waterloo's RoboHub.

Extra

I am a part of UBC's Graduate Student Society (GSS), representing the Department of Electrical and Computer Engineering (ECE) as a GSS Councillor. We advocate on behalf of graduate students to the university and the broader community.

Outside of work, I am fond of hiking, photography, and graphic design. You can also often find me gaming, cooking, or talking about coffee.

News

May. '26

Sep. '25  

Jan. '25  

Sep. '24  

Jun. '23  

In this paper, we presented an adaptive PD control scheme for limiting robot energy during pHRI. This work won the Best Paper Award in Robotics at CRV 2026.

My Master's Thesis, submitted to the Department of Mechanical and Manufacturing Engineering at the University of Calgary. Presents a novel energy-conscious controller for safe pHRI.

My undergraduate thesis, submitted to the Department of Electrical Engineering at SEECS, NUST. We present a robotic setup that can learn to grasp geometrically diverse objects using self-supervision.

• ELEC 571 - Computational Control (COCO)

• ENME 505 - Robotics
• ENME 501/502 - Mechanical Engineering Capstone Project 1/2

• SE 807 - Machine Learning
• CS 470 - Machine Learning

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