  Robotic solutions, in particular robotic arms, are becoming more frequently
deployed for close collaboration with humans, for example in manufacturing or
domestic care environments. These robotic arms require the user to control
several Degrees-of-Freedom (DoFs) to perform tasks, primarily involving
grasping and manipulating objects. Standard input devices predominantly have
two DoFs, requiring time-consuming and cognitively demanding mode switches to
select individual DoFs. Contemporary Adaptive DoF Mapping Controls (ADMCs) have
shown to decrease the necessary number of mode switches but were up to now not
able to significantly reduce the perceived workload. Users still bear the
mental workload of incorporating abstract mode switching into their workflow.
We address this by providing feed-forward multimodal feedback using updated
recommendations of ADMC, allowing users to visually compare the current and the
suggested mapping in real-time. We contrast the effectiveness of two new
approaches that a) continuously recommend updated DoF combinations or b) use
discrete thresholds between current robot movements and new recommendations.
Both are compared in a Virtual Reality (VR) in-person study against a classic
control method. Significant results for lowered task completion time, fewer
mode switches, and reduced perceived workload conclusively establish that in
combination with feedforward, ADMC methods can indeed outperform classic mode
switching. A lack of apparent quantitative differences between Continuous and
Threshold reveals the importance of user-centered customization options.
Including these implications in the development process will improve usability,
which is essential for successfully implementing robotic technologies with high
user acceptance.
