  The development of assistive robotic agents to support household tasks is
advancing, yet the underlying models often operate in virtual settings that do
not reflect real-world complexity. For assistive care robots to be effective in
diverse environments, their models must be robust and integrate multiple
modalities. Consider a caretaker needing assistance in a dimly lit room or
navigating around a newly installed glass door. Models relying solely on visual
input might fail in low light, while those using depth information could avoid
the door. This demonstrates the necessity for models that can process various
sensory inputs. Our ongoing study evaluates state-of-the-art robotic models in
the AI2Thor virtual environment. We introduce disturbances, such as dimmed
lighting and mirrored walls, to assess their impact on modalities like movement
or vision, and object recognition. Our goal is to gather input from the
Geriatronics community to understand and model the challenges faced by
practitioners.
