Most cyber-physical human systems (CPHS) rely on users learning how to interact with the system. Rather, a collaborative CPHS should learn from the user and adapt to them in a way that improves holistic system performance. Accomplishing this requires collaboration between the human-robot/human-computer interaction and the cyber-physical system communities in order to feed back knowledge about users into the design of the CPHS. The requisite user studies, however, are difficult, time consuming, and must be carefully designed. Furthermore, as humans are complex in their interactions with autonomy it is difficult to know, a priori, how many users must participate to attain conclusive results. In this research theme we are working toward a new strategy that augments current shared control, and provide a mechanism to directly feed back results from the HRI community into autonomy design.
Inferring User Qualities in Shared Control of Robots
In this project we seek to infer intrinsic user qualities through human-robot interactions correlated with robot performance in order to adapt the autonomy and improve holistic CPHS performance. We demonstrate through a study that this idea is feasible. Next, we demonstrate that signiﬁcant diﬀerences between groups of users can impact conclusions particularly where diﬀerent autonomies are involved. Finally, we also provide our rich, extensive corpus of user study data to the wider community to aid researchers in designing better CPHS.
To obtain this data please send an email request to email@example.com.
- L. Hall, U. Acharya, J. Bradley, and B. Duncan, “Inference of User Qualities in Shared Control of CPHS: A Contrast in Users,” in 2018 IFAC Cyber-Physical Human Systems, Miami, FL, 2018.
- U. Acharya, S. Kunde, L. Hall, B. Duncan, and J. Bradley, “Inference of User Qualities in Shared Control,” in 2018 IEEE International Conference on Robotics and Automation, Brisbane, Australia, 2018, pp. 588–595.
Predicting Loss of Control in Piloted UAS
The NIMBUS Lab is conducting research into pilot loss of control, which is a leading cause of UAS crashes. We have developed a novel control authority switching system that assesses the potential for pilot loss of control and makes a decision about whether or not to hand control to a provably safe computer control system to avoid loss of control and a resulting crash. The switching algorithm models the pilot-UAS interaction as a Markov Decision Process (MDP), accounting for the stochastic nature of the user’s control inputs and vehicle state. To train the MDP we utilized 13 UAS pilots ranging in skill level and conducted 103 flights of varying difficulty, collecting all position and orientation data alongside user skill level. We make this corpus of data publicly available to encourage more research into UAS loss of control and crash events.