Abstract:
Human-in-the-loop control for mobile robots is an important aspect of robot operation, especially for navigation in unstructured environments or in the case of unexpected events. However, traditional paradigms of human-in-the-loop control have relied heavily on the human to provide precise and accurate control inputs to the robot, or reduced the role of the human to providing supervisory task specifications. In this thesis, we explore a new paradigm of human-in-the-loop control, where the robot can act semi-autonomously according to the human’s intention while having the human directly control the motion of the robot. The proposed paradigm maximizes the strengths of the human and robot such that the human-robot system can perform at optimal efficiency.

To this end, we first abstract away difficult vehicle dynamics by way of motion primitive teleoperation, which allows an operator to control a vehicle with guaranteed dynamic feasibility. We then build upon motion primitive teleoperation and present a method for reactive collision avoidance. We then propose a novel method of local trajectory generation without end goal specifications for human-in-the-loop control. The method, called Biased Incremental Action Sampling, is a sample based approach to build motion primitive trees that optimize for non-goal based cost functions. We then introduce hierarchical human-in-the-loop planning, which incorporates intended motions as global paths such that generated local trajectories can follow the paths autonomously. Lastly, we introduce continuous dynamic autonomy by generating path predictions on semantic topological navigation maps. By incorporating environment contexts into human-in-the-loop control, this allows us to reason about the human’s intentions over the path space and generate predictions to assist navigation in unstructured, constrained environments.

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Thesis Committee Members:
Nathan Michael, Chair
Jean Oh
Henny Admoni
Helen Oleynikova, NVIDIA
Sanjiban Choudhury, Aurora Innovation