Reinforcement Learning Based Assistive Robotic Arm Control and Path Planning Automization

This project aims to make assistive robots smarter and easier to use for people with limited mobility. Researchers will develop a new type of control system for a wheelchair-mounted robotic arm that helps users complete everyday tasks like grabbing objects, opening drawers, or pressing buttons. Using advanced artificial intelligence, the system will learn through realistic computer simulations how to adjust automatically to changes, such as different object weights or the tilt of a wheelchair, without needing manual fine-tuning. The research will be done in collaboration with Sielo Robotics, an Ottawa-based company focused on creating affordable assistive technology. The results will help Sielo improve the reliability and usability of their robotic arm, making it more adaptable for real-world use. Ultimately, this project supports greater independence for people with disabilities while strengthening Canada’s leadership in robotics and accessibility innovation.

Faculty Supervisor:

Arash Arami

Student:

Partner:

Sielo Robotics

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology; Manufacturing

University:

University of Waterloo

Program: