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 thesis discusses the use of Bayesian Inference in inferring over the human's objective for Human-Robot Interaction, more specifically, it focuses upon the adaptation of methods to better utilize the information for inferring upon the human's objective for Reward Learning and Communicative Shared Autonomy settings. To accomplish this, we first examine state-of-the-art methods for approaching Bayesian Inverse Reinforcement learning where we explore the strengths and weaknesses of current approaches. After which we explore alternative methods for approaching the problem, borrowing similar approaches to those of the statistics community to apply alternative methods to improve the sampling process over the human's belief. After this, I then move to a discussion on the setting of Shared Autonomy in the presence and absence of communication. These differences are then explored in our method for inferring upon an environment where the human is aware of the robot's intention and how this can be used to dramatically improve the robot's ability to cooperate and infer upon the human's objective. In total, I conclude that the use of these methods to better infer upon the human's objective significantly improves the performance and cohesion between the human and robot agents within these settings. / Master of Science / This thesis discusses the use of various methods to allow robots to better understand human actions so that they can learn and work with those humans. In this work we focus upon two areas of inferring the human's objective: The first is where we work with learning what things the human prioritizes when completing certain tasks to better utilize the information inherent in the environment to best learn those priorities such that a robot can replicate the given task. The second body of work surrounds Shared Autonomy where we work to have the robot better infer what task a human is going to do and thus better allow the robot to assist with this goal through using communicative interfaces to alter the information dynamic the robot uses to infer upon that human intent. Collectively, the work of the thesis works to push that the current inference methods for Human-Robot Interaction can be improved through the further progression of inference to best approximate the human's internal model in a given setting.

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.