Assistive robots have the potential to enable persons with motor disabilities to live more independent lives. Object retrieval has been rated a high-priority task for assistive robots. A key challenge in creating effective assistive robots lies in designing control interfaces that enable the human user to control the robot. This thesis builds on prior work that uses a laser pointer to allow the person to intuitively communicate their goals to a robot by creating a `clickable world'. Specifically, this thesis reduces the infrastructure needed for the robot to recognize the user's goal by augmenting the laser pointer with a small camera, an inertial measurement unit (IMU), and a laser rangefinder to estimate the location of the object to be grasped. The robot then drives to the approximate target location given by input from the laser pointer while using an onboard camera to detect an object near the target location. Local autonomy on the robot is used to visually navigate to the detected object to enable object retrieval. Results show a successful proof of concept in demonstrating reasonable detection of user intent on a 1.23 x 1.83 meters squared test grid. Testing of the estimation of object location in the odometry frame fell within range of successful local autonomy object retrieval for an environment with a single object. Future work includes testing on a wide variety of dropped objects and in cluttered environments which is needed to validate the effectiveness of the system for potential end users.

In this thesis, we present research on synthesizing autonomous driving with shared autonomy using Unity Engine. Adaptive Cruise Control (ACC) is considered as level 1 autonomous vehicle, which has been studied by academia and commercialized by industry. Cooperative Adaptive Cruise Control (CACC) system is an expansion of ACC, in which communication is set up between members to share driving information. Shared autonomy is a subject about human-computer interactivities. In our research, we developed a highly customizable 3D environment. We can simulate various driving scenarios and analyze the performance of different driving methods from human driving to CACC. The result of simulation proves the safety and efficiency of CACC, and the project also provides a potential of assisting the improvement of autonomous vehicles.

This work analyzes human-in-the-loop robotic systems to determine where human input can be most beneficial to a collaborative task. This is accomplished by implementing a pick-and-place task using a human-in-the-loop robotic system and determining which segments of the task, when replaced by human guidance, provide the most improvement to overall task performance and require the least cognitive effort. The first experiment entails implementing a pick and place task on a commercial robotic arm. Initially, we look at a pick-and-place task that is segmented into two main areas: coarse approach towards a goal object and fine pick motion. For the fine picking phase, we look at the importance of user guidance in terms of position and orientation of the end effector. Results from this initial experiment show that the most successful strategy for our human-in-the-loop system is the one in which the human specifies a general region for grasping, and the robotic system completes the remaining elements of the task. We extend this study to include a second experiment, utilizing a more complex robotic system and pick-and-place task to further analyze human impact in a human-in-the-loop system in a more realistic setting. In this experiment, we use a robotic system that utilizes an Xbox Kinect as a vision sensor, a more cluttered environment, and a pick-and-place task that we segment in a way similar to the first experiment. Results from the second experiment indicate that allowing the user to make fine tuned adjustments to the position and orientation of the robotic hand can improve task success in high noise situations in which the autonomous robotic system might otherwise fail. The experimental setups and procedures used in this thesis can be generalized and used to guide similar analysis of human impact in other human-in-the-loop systems performing other tasks.