This paper describes an implementation of the 3T robot architecture which has been under development for the last eightyears. The architecture uses three levels of abstraction and description languages whichare compatible between levels. The makeup of the architecture helps to coordinate planful activities with real-time behaviors for dealing with dynamic environments. In recent years, other architectures have been created with similar attributes but two features distinguish the 3T architecture: 1) a variety of useful software tools have been created to help implement this architecture on multiple real robots;, and 2) this architecture, or parts of it, have been implemented on a varietyofvery different robot systems using different processors, operating systems, effectors and sensor suites.

firestorm of interest in autonomous robots with the introduction of the Subsumption architecture 1 [Brooks86]. At the time, the dominant view in the AI community was that a control system for an autonomous mobile robot should be decomposed into three functional elements: a sensing system, a planning system, and an execution system [Nilsson80]. The job of the sensing system is to translate raw sensor input (usually sonar or vision data) into a world model. The job of the planner is to take the world model and a goal and generate a plan to achieve the goal. The job of the execution system is to take the plan and generate the actions it prescribes. The sense-plan-act (SPA) approach has two significant architectural features. First, the flow of

Robot systems must achieve high level goals while remaining reactive to contingencies and new opportunities. This typically requires robot systems to coordinate concurrent activities, monitor the environment, and deal with exceptions. We have developed a new language to support such task-level control. The language, TDL, is an extension of C++ that provides syntactic support for task decomposition, synchronization, execution monitoring, and exception handling. A compiler transforms TDL into pure C++ code that utilizes a platform-independent task management library. This paper introduces TDL, describes the task tree representation that underlies the language, and presents some aspects of its implementation and use in an autonomous mobile robot. Introduction Robot systems, such as autonomous mobile robots, need to achieve high level goals while remaining reactive to contingencies and new opportunities. They need to recover gracefully from exceptions and effectively manage their resourc...

Problem-solving methods for knowledge-based systems establish the behavior of such systems by defining the roles in which domain knowledge is used and the ordering of inferences. Developers can compose problem-solving methods that accomplish complex application tasks from primitive, reusable methods. The key steps in this development approach are task analysis, method selection "from a library", and method configuration.

This paper describes an extension to the rap system task-net semantics and representation language to enable the effective control of continuous processes. The representation addresses the problems of synchronizing plan expansion with events in the world, coping with multiple, non-deterministic task outcomes, and the description of a simple form of clean-up task. It is also pointed out that success and failure need no special place in a task network representation. Success and failure are really messages about the execution system's knowledge and do not explicitly define that system's flow of control. To Appear in the Second International Conference on AI Planning Systems, June 1994. 1 Introduction Recently, AI researchers have proposed several different mechanisms for programming robots reactively. These include collections of behaviors [2], schemas [1], routines [9], and reflexes [15]. Many details differ between these proposals, particularly in the area of philosop...

Vision systems that have successfully supported nontrivial tasks have invariably taken advantage of constraints derived from the task and environment to increase reliability and lower the complexity of perception. We propose that it is possible to build a general purpose vision system, that is, one that can support a wide variety of tasks, and take advantage of such constraints. The central idea within our proposed architecture is the reactive skill. Skills are concurrent control routines assembled at run time using instructions from a symbolic execution system. Visual modules are used as resources in the construction of these skills. Skills control the agent as continuous feedback loops but are constructed using discrete, symbolic instructions. The key to general-purpose vision is the ability to parametrize the primitive elements of the vision system and to compose visual and control routines in a variety of ways. We demonstrate the architecture in the context of an implemented examp...

This paper is about the interface between continuous and symbolic robot control. We advocate describing continuous actions and their related sensing strategies as situation specific activities, which can be manipulated by a symbolic reactive planner. The approach addresses the issues involved in turning symbolic actions into continuous activities, and using task specific sensing routines to support those activities. Situation specific activities help preserve the convenient fiction of "primitive actions" for use in planning without requiring that they all be programmed into the control system in advance. We demonstrate the utility of this architecture with an object tracking example. A control system is presented that can be reconfigured by a the rap reactive executor to achieve different tasks. We show how this system allows us to build interchangeable tracking activities that use different sensing /action feedback loops in different situations. First Internation...

This paper appeared in Trends in Cognitive Science, Vol. 2, No. 3, March 1998, 82-87.)

Teamwork demands agreement among teammembers to collaborate and coordinate effectively. When a disagreement between teammates occurs (due to failures), team-members should ideally diagnose its causes, to resolve the disagreement. Such diagnosis of social failures can be expensive in communication and computation overhead, which previous work did not address. We present a novel design space of diagnosis algorithms, distinguishing several phases in the diagnosis process, and providing alternative algorithms for each phase. We then combine these algorithms in different ways to empirically explore specific design choices in a complex domain, on thousands of failure cases. The results show that centralizing the diagnosis disambiguation process is a key factor in reducing communications, while run-time is affected mainly by the amount of reasoning about other agents. These results contrast sharply with previous work in disagreement detection, in which distributed algorithms reduce communications.

This proposal discusses the use of the intentions of the actor in performing meansend reasoning. In doing so, it will show that preconditions and applicability conditions in existing systems are ill-defined and intrinsically encode situational information that prevents intentions from playing a role in the planning process. While the former problem can be fixed, the latter cannot. Therefore, I argue that preconditions should be eliminated from action representation. In their place, I suggest explicit representation of intention, situated reasoning about the results of action, and robust failure mechanisms. I then describe a system, the Intentional Planning System (ItPlanS), which embodies these ideas, compare ItPlanS to other systems, and propose future directions for this work. Contents 1 Introduction 2 1.1 Thesis Statement : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 3 1.2 Outline of Proposal : : : : : : : : : : : : : : : : : : : : : : : : : : : : 4 2 Preconditions and...