n a problem where a human uses supervisory control to manage robot path-planning, there are times when human does the path planning, and if satisﬁed commits those paths to be executed by the robot, and the robot executes that plan. In planning a path, the robot often uses an optimization algorithm that maximizes or minimizes an objective. When a human is assigned the task of path planning for robot, the human may care about multiple objectives. This work proposes a graphical user interface (GUI) designed for interactive robot path-planning when an operator may prefer one objective over others or care about how multiple objectives are traded oﬀ. The GUI represents multiple objectives using the metaphor of an artist’s palette. A distinct color is used to represent each objective, and tradeoﬀs among objectives are balanced in a manner that an artist mixes colors to get the desired shade of color. Thus, human intent is analogous to the artist’s shade of color. We call the GUI an “Adverb Palette” where the word “Adverb” represents a speciﬁc type of objective for the path, such as the adverbs “quickly” and “safely” in the commands: “travel the path quickly”, “make the journey safely”. The novel interactive interface provides the user an opportunity to evaluate various alternatives (that tradeoﬀ between diﬀerent objectives) by allowing her to visualize the instantaneous outcomes that result from her actions on the interface. In addition to assisting analysis of various solutions given by an optimization algorithm, the palette has additional feature of allowing the user to deﬁne and visualize her own paths, by means of waypoints (guiding locations) thereby spanning variety for planning. The goal of the Adverb Palette is thus to provide a way for the user and robot to ﬁnd an acceptable solution even though they use very diﬀerent representations of the problem. Subjective evaluations suggest that even non-experts in robotics can carry out the planning tasks with a great deal of ﬂexibility using the adverb palette.
Improvements in robot autonomy are changing the human-robot interaction from low-level manipulation to high-level task-based collaboration. For a task-oriented collaboration, a human assigns sub-tasks to robot team members. In this paper, we consider task-oriented collaboration of humans and robots in a cordon and search problem. We focus on a path-planning framework with natural language input. By the semantic elements in a shared mental model, a natural language command can be converted into optimization objectives. We import multi-objective optimization to facilitate modeling the “adverb” elements in natural language commands. Finally, human interactions are involved in the optimization search process in order to guarantee that the found solution correctly reflects the human’s intent.