A major challenge for robots that provide physical assistance is adapting to the needs of different people. To overcome this, personalised assistive models can be created by observing the demonstrations of help provided by an assistant, a setting known as Learning Assistance by Demonstration (LAD). In this work, the case of robotic wheelchairs and drivers with hand control disabilities, which make navigation more challenging, was considered. To better understand LAD and its features, a simulator capable of generating repeatable examples of the triadic interactions between drivers, robots, and assistants was developed. The software is designed to be modular and parametrisable, enabling customisation and experimentation with various synthetic disabilities and scenarios. This approach was employed to design more effective data collection procedures and to enhance learning models. With these, it is shown that, at least in simulation, LAD can be used as follows: for different disabilities; to help consistently; to generalise to physically different environments; and to create customised assistive policies. In summary, the results provide further evidence that LAD is a viable approach for efficiently creating personalised assistive solutions for robotic wheelchairs.