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Modeling human expertise for providing adaptive levels of robot shared autonomy
In shared autonomy, a robot and human user both have some level of control in order to achieve a shared goal. Choosing the balance of control given to the user and the robot can be a challenging problem since different users have different preferences and vary in skill levels when operating a robot. We propose using a novel formulation of Partially Observable Markov Decision Processes (POMDPs) to represent a model of the user's expertise in controlling the robot. The POMDP uses observations from the user's actions and from the environment to update the belief of the user's skill and chooses a level of control between the robot and the user. The level of control given between the user and the robot is encapsulated in macro-action controllers. The macro-action controllers encompass varying levels of robot autonomy and reduce the space of the POMDP, removing the need to plan over separate actions. As part of this research, we ran two users study, developed a method to automatically generate macro-action controller values, and applied our user expertise model to provide shared autonomy on a semi-autonomous underwater vehicle. In our first user study, we tested our user expertise model in a robot driving simulation. Users drove a simulated robot through an obstacle-filled map while the POMDP model chose appropriate macro-action controllers based on the belief state of the user's skill level. The results of the user study showed that our model can encapsulate user skill levels. The results also showed that using the controller with greater robot autonomy helped users of low skill avoid obstacles more than it helped users of high skill. We designed a controller value synthesis method to generate the variables that control the levels of autonomy in the macro-action controllers. We found differences in how the users drive the robot using a decision tree generated from the data recorded in the first user study, and we used these differences to program simulated user ``bots'' that mimic users of different skill levels. The ``bots'' were used to test a range of variables for the controllers, and the controller variables were found from minimizing obstacles hit, time to complete maps, and total distance driven from the simulated data.For our second user study, we looked at users' satisfaction without robot autonomy, with the highest amount of autonomy, and with the autonomy chosen by our expertise model. We found users we classified as beginners ranked the autonomy more favorably than those ranked as experts. We implemented our expertise model on a Seabotix vLBV300 underwater vehicle and ran a trial off the coast of Newport, Oregon. During our trials, we recorded a user driving the vehicle to predetermined waypoints. When beginner actions were performed, the user expertise model provided an increased level of autonomy which either increased throttle when far from waypoints or decreased throttle when close to waypoints. This demonstrated an implementation of our algorithm on existing robot hardware in the field.Keywords: Shared autonomy, Robotics, Human-robot interactio
A Novel Predictor Based Framework to Improve Mobility of High Speed Teleoperated Unmanned Ground Vehicles
Teleoperated Unmanned Ground Vehicles (UGVs) have been widely used in applications when driver safety, mission eciency or mission cost is a major concern. One major challenge with teleoperating a UGV is that communication delays can significantly affect the mobility performance of the vehicle and make teleoperated driving tasks very challenging especially at high speeds.
In this dissertation, a predictor based framework with predictors in a new form and a blended architecture are developed to compensate effects of delays through signal prediction, thereby improving vehicle mobility performance. The novelty of the framework is that minimal information about the governing equations of the system is required to compensate delays and, thus, the prediction is robust to modeling errors.
This dissertation first investigates a model-free solution and develops a predictor that does not require information about the vehicle dynamics or human operators' motion for prediction. Compared to the existing model-free methods, neither assumptions about the particular way the vehicle moves, nor knowledge about the noise characteristics that drive the existing predictive filters are needed. Its stability and performance are studied and a predictor design procedure is presented.
Secondly, a blended architecture is developed to blend the outputs of the model-free predictor with those of a steering feedforward loop that relies on minimal information about vehicle lateral response. Better prediction accuracy is observed based on open-loop virtual testing with the blended architecture compared to using either the model-free predictors or the model-based feedforward loop alone.
The mobility performance of teleoperated vehicles with delays and the predictor based framework are evaluated in this dissertation with human-in-the-loop experiments using both simulated and physical vehicles in teleoperation mode. Predictor based framework is shown to provide a statistically significant improvement in vehicle mobility and drivability in the experiments performed.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146026/1/zhengys_1.pd