Resolving Over-subscribed Temporal Planning Problems through Fluent Human-Robot Collaboration

There has been a rapid growth in the deployment of robots in the past decade. From aircraft assembly to home cleaning, robots have been working for us in many domains of our lives. However, there is one important barrier that limits their performance: these autonomous systems become very brittle when they fail to achieve what they were asked to do. No matter it is a sophisticated manufacturing robot, or a simple navigation app, little support can be provided from them for resolving the failures. Such over-subscribed situations are either caused by the robot’s incomplete knowledge about available actions for all objectives, or inconsistent goals given by the human. For example, a factory manager may be given an overly ambitious production goal, which cannot be accomplished given the limited time and equipment in the factory. Due to the scale and complexity of the planning tasks in real world problems, it is nearly impossible for humans to find resolutions independently. Unfortunately, the automatic planners used to control these robots cannot provide any useful assistance but signal failure in such situations. This is the essential issue that I would like to address in my PhD thesis: to develop an interactive decision support system that works like an intelligent and trustworthy teammate that can support humans in these situations. The autonomous agent, called Uhura, collaborates with humans to resolve over-subscribed planning problems. The key idea is to reduce the problem to a scale that can be easily handled by the humans, with fewer decisions and constraints to consider. Uhura works like an expert in plan diagnosis, which (1) automatically detects the cause of failure (2) succinctly explains them to the users and (3) presents preferred resolutions for repairing the broken plans. Only information that is key to the resolution will be communicated. In addition, Uhura also asks for inputs from the human during the collaborations. Due to the limitation of the decision aid, it may not be aware of all available options and the users’ preference over different resolutions. The collaboration will help Uhura gain additional knowledge and resolve problems beyond its domain of knowledge. Uhura is an integrated system with three core modules: a temporal planner for plan generation, a tester for validating plan consistency, and a user interface for all communications. The planner and tester are supported by a conflict-directed relaxation algorithm that can diagnose over-constrained plans: first detect conflicts of inconsistent goals and incomplete planning domains, then generate resolutions to address the conflicts and incompleteness. The resolutions are alternative plans, each of which relaxed some of the goals and planning domains. After diagnosis, Uhura will initialize a dialog with the user to explain the cause of failure and present resolutions. The user can either take Uhuras advice, or ask Uhura to find different alternatives with a reason. This failure-driven dialog is initialized each time one or more conflicts are detected that threaten the task plan, during both planning and execution stages. In my thesis, I will be focusing on the application of Uhura in the following scenarios, which are in the domains of automated manufacturing and logistics planning: • Personal Transportation: Uhura serves as a personal transit advisor to find and economic ways to commute in cities, and a management tool for public transit providers to reduce passenger flow during peak hours. • Deep-sea Explorations: Uhura is incorporated as part of a mission advisor for oceanographers to manage high-risk expeditions. It assists the scientists in planning activities with large temporal uncertainty, and rescheduling tasks in unexpected failure situations using available resources. • Factory-floor Manufacturing: Uhura is integrated in a production advisor for planning and scheduling activities on the factory floor. It provides quick assistance for repairing broken plans due to conflicting goals and equipments, and failures occurred in operations.