Wheelchair-mounted robotic manipulators have the potential to help the elderly and individuals living with disabilities carry out their activities of daily living independently. While robotics researchers focus on assistive tasks from the perspective of various control schemes and motion types, health research tends to concentrate on clinical assessment and rehabilitation. This difference in perspective often leads to the design and evaluation of experimental tasks that are tailored to specific robotic capabilities rather than solving tasks that would support independent living.

In addition, there are many studies in healthcare on which activities are relevant to functional independence, but little is known about how often these activities occur. Understanding which activities are frequently carried out during the day can help guide the development and prioritization of assistive robotic technology. By leveraging the strength of robotics (i.e., performing well on repeated tasks) these activities can be automated, significantly improving the quality of life for our target population.

Our first research goal is to investigate daily task frequency data in order to provide deeper insights and meaningful guidelines for future research developments in the field of assistive robotic manipulation. These guidelines are meant to shift focus towards better supporting the needs and performance requirements of the target population. While we have established that assistive robotic manipulators can help individuals regain functional independence, their performance is restricted by the underlying control system. Aside from having direct control over the robots motion, each manipulator comes with a limited preprogrammed set of modes (e.g., drinking mode) that might not fully encompass the individual's needs. For mainstream use and acceptance, it is paramount to provide the target population with a way to refine the control and customize it to their personal needs. One method for learning new behaviours and skills on the fly is interactive shaping.

Interactive shaping is a method of transferring knowledge from a human to a learning agent by having the human teacher provide signals of approval (or disapproval) from instantaneous observations of the robots behaviour. This could be of great value in an assistive setting as it does not require the teacher to have expert domain knowledge. The teacher only needs to give feedback on whether the robot's previous action was "good" or "bad" in order to teach it new behaviours and skills, regardless of task difficulty. Our second research goal is to investigate whether interactive shaping can be used to teach robotic manipulators new autonomous tasks. To this end, we adapt TAMER, a framework for learning from human reward signals, to a seven degree of freedom robotic manipulator and carry out a proof-of-concept user study.

The work in this thesis is meant to open an avenue to better align research with the needs of individuals that will eventually leverage the technology in their daily life. To do so, we first bridge the gap between robotics and healthcare research to align both with respect to the target population's needs. Taking it one step further, we then adapt TAMER to allow the users themselves to add new autonomous behaviours to their wheelchair-mounted robotic manipulator. Together, these results introduce a new level of long-term autonomy for individuals living with disabilities.

We gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada (NSERC) and Alberta Innovates.

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