Human-in-the-loop Planning of Mobile Robots

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-robot collaboration called human-in-the-loop planning, where the robot can act semi-autonomously according to the human's intention while having the human directly inform motion planning and trajectory generation. The proposed paradigm maximizes the strengths of the human and robot such that the human-robot system can perform with optimal efficiency.

To this end, we first abstract away complex vehicle dynamics by way of motion primitive teleoperation, which allows an operator to control a vehicle via input reparameterization into trajectories. 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.