Plan Projection, Execution, and Learning for Mobile Robot Control

Most state-of-the-art hybrid control systems for mobile robots are decomposed into different layers. While the deliberation layer reasons about the actions required for the robot in order to achieve a given goal, the behavioral layer is designed to enable the robot to quickly react to unforeseen events. This decomposition guarantees a safe operation even in the presence of unforeseen and dynamic obstacles and enables the robot to cope with situations it was not explicitly programmed for. The layered design, however, also leaves us with the problem of plan execution. The problem of plan execution is the problem of arbitrating between the deliberation- and the behavioral layer. Abstract symbolic actions have to be translated into streams of local control commands. Simultaneously, execution failures have to be handled on an appropriate level of abstraction. It is now widely accepted that plan execution should form a third layer of a hybrid robot control system. The resulting layered architectures are called three-tiered architectures, or 3T architectures for short. Although many high level programming frameworks have been proposed to support the implementation of the intermediate layer, there is no generally accepted algorithmic basis for plan execution in three-tiered architectures. In this thesis, we propose to base plan execution on plan projection and learning and present a general framework for the self-supervised improvement of plan execution. This framework has been implemented in APPEAL, an Architecture for Plan Projection, Execution And Learning, which extends the well known RHINO control system by introducing an execution layer. This thesis contributes to the field of plan-based mobile robot control which investigates the interrelation between planning, reasoning, and learning techniques based on an explicit representation of the robot's intended course of action, a plan. In McDermott's terminology, a plan is that part of a robot control program, which the robot cannot only execute, but also reason about and manipulate. According to that broad view, a plan may serve many purposes in a robot control system like reasoning about future behavior, the revision of intended activities, or learning. In this thesis, plan-based control is applied to the self-supervised improvement of mobile robot plan execution

Learning Structured Reactive Navigation Plans from Executing MDP Navigation Policies

Autonomous robots, such as robot office couriers, need navigation routines that support flexible task execution and effective action planning. This paper describes XFRMLEARN, a system that learns structured symbolic navigation plans. Given a navigation task, XFRMLEARN learns to structure continuous navigation behavior and represents the learned structure as compact and transparent plans. The structured plans are obtained by starting with monolithical default plans that are optimized for average performance and adding subplans to improve the navigation performance for the given task. Compactness is achieved by incorporating only subplans that achieve significant performance gains. The resulting plans support action planning and opportunistic task execution. XFRMLEARN is implemented and extensively evaluated on an autonomous mobile robot

ABSTRACT Learning Structured Reactive Navigation Plans from Executing MDP Navigation Policies

Autonomous robots, such as robot office couriers, need navigation routines that support flexible task execution and effective action planning. This paper describes XFRMLEARN, a system that learns structured symbolic navigation plans. Given a navigation task, XFRMLEARN learns to structure continuous navigation behavior and represents the learned structure as compact and transparent plans. The structured plans are obtained by starting with monolithical default plans that are optimized for average performance and adding subplans to improve the navigation performance for the given task. Compactness is achieved by incorporating only subplans that achieve significant performance gains. The resulting plans support action planning and opportunistic task execution. XFRMLEARN is implemented and extensively evaluated on an autonomous mobile robot. 1

Experience- and Model-based Transformational Learning of Symbolic Behavior Specifications

This paper describes XFRMLEARN,a system that learns symbolic behavior specifications to control and improve the continuous sensor-driven navigation behavior of an autonomous mobile robot. The robot is to navigate between a set of predefined locations in an office environment and employs a navigation system consisting of a path planner and a reactive collision avoidance system. XFRMLEARN rationally reconstructs the continuous sensor-driven navigation behavior in terms of task hierarchies by identifying significant structures and commonalities in behaviors. It also constructs a statistical behavior model for typical navigation tasks. The behavior model together with a model of how the collision avoidance module should "perceive" the environment is used to detect behavior "flaws," diagnose them, and revise the plans to improve their performance. The learning method is implemented on an autonomous mobile robot. Introduction Many state-of-the-art navigation systems consider navigation as a..

XFRMLearn: A System for Learning Structured Reactive Navigation Plans

Abstract. Autonomous robots, such as robot office couriers, need navigation routines that support flexible task execution and effective action planning. This paper describes XFRMLEARN, a system that learns structured symbolic navigation plans. Given a navigation task, XFRMLEARN learns to structure continuous navigation behavior and represents the learned structure as compact and transparent plans. The structured plans are obtained by starting with monolithic default plans that are optimized for average performance and adding subplans to improve the navigation performance for the given task. Compactness is achieved by incorporating only subplans that achieve significant performance gains. The resulting plans support action planning and opportunistic task execution. XFRMLEARN is implemented and extensively evaluated on an autonomous mobile robot.

Learning to Optimize Mobile Robot Navigation Based on HTN Plans

High-level symbolic representations of actions to control the working of autonomous robots are used in all hybrid (reactive and deliberative) robot control architectures. Abstract action representations serve several purposes, such as structuring the control code, optimizing the robot performance, and providing a basis for reasoning about future robot action

Plan Projection under the APPEAL Robot Control Architecture

Abstract. The paper presents APPEAL, a three-layer mobile robot control Architecture for Projection-based Planning, Execution and Learning, with a focus on its execution layer. Besides task decomposition and failure recovery, it supports the projection of navigation plans based on learned models of navigation actions. Plan projection is applied to detect opportunities for improving the robot’s navigation plan and transforming it accordingly. In the experimental section, we quantify the performance gains achieved by applying these techniques, both in simulation and on a robot. The savings are considerable.