Antonius J. Hendriks

Planning for reactive robot behavior

A robot system operating in an environment in which there is uncertainty and change needs to combine the ability to react with the ability to plan ahead. The authors had previously proposed a solution to the problems of integrating planning and reaction: cast planning as adaptation of a reactive system. They extend the theoretical treatment of the planner, including the effect of its iterated adaptation on the reactor, and describe the current implementation environment and a working example.<<ETX>>

A practical approach to integrating reaction and deliberation

Abstract A robot system operating in an environment in which there is uncertainty and change needs to integrate the ability to react with the ability to plan ahead. In this paper, we present a novel approach to this integration for a practical robot problem, the kitting robot. In our approach, planning is cast as the ongoing adaptation of a reactive system, incrementally bringing its behavior into line with a set of goals. A method is presented whereby the planner can make iterative improvements to the reactor so that it eventually converges on the ideal reactor (a concept related to the universal plan) for its environment and set of planner goals. To balance this theoretical work, our current implementation on a Puma 560 robot is overviewed.

Planning as incremental adaptation of a reactive system

Abstract The importance of solving the problem of integrating deliberative (“planning”) capabilities and reactive capabilities when building robust, ‘real-world’ robot systems is becoming widely accepted (Bresina and Drummond, 1990; Fraichard and Laugier, 1991; McDermott, 1991). This paper presents a solution to this problem: cast planning as the incremental adaptation of a reactive system to suit changes in goals or the environment. Our application domain is a manufacturing problem - robotic kitting. This paper represents an advance on existing work in two ways: It presents and formally examines an architecture that incorporates the benefits of a deliberative component without compromising the reactive component. Secondly, it provides the first set of performance statistics in the literature for this class of system. In our approach, the reactive system (the reactor) is a real-time system that continually interacts with the environment, and the planner is a separate and concurrent system that incrementally ‘tunes’ the behavior of the reactor to ensure that goals are achieved. We call this the planner-reactor approach. The reactor is described using a formal framework for representing flexible robot plans, the RS model (Lyons, 1990; Lyons and Arbib, 1989). Thus, the behavior of the reactor, and the rules by which the reactor can be modified, become open to mathematical analysis. We employ this to determine the constraints the planner must abide by to make safe adaptations and to ensure that incremental adaptations converge to a desired reactor. We discuss our current implementation of planner and reactor, work through an example from the kitting robot application, and present implementation results.

Achieving robustness by casting planning as adaptation of a reactive system

Classical artificial intelligence planning is not sufficiently robust in uncertain and dynamic environments. Reactive approaches are robust in some environments-namely those for which they have been programmed. An approach that integrates a priori planning with reaction to increase robustness is presented. As motivation, a practical robot problem, the kitting robot, is presented. Solving this problem demands a system that can make timely and robust actions in an uncertain environment. A solution to the kitting robot problem in which planning is cast as adaptation of a reactive system to suit changes in the goals or environment is outlined. The reactive system (the reactor) is based on a formal model for representing flexible robot plans, the RS model. Thus, it was possible to formalize the mechanisms by which the planner improves the behavior of the reactor. This system was implemented to control a Puma-560 robot equipped with visual sensing.<<ETX>>

Exploiting patterns of interaction to achieve reactive behavior

Abstract This paper introduces an approach that allows an agent to exploit inherent patterns of interaction in its environment, so-called dynamics, to achieve its objectives. The approach extends the standard treatment of planning and (re) action in which part of the input to the plan generation algorithm is a set of basic actions and perhaps some domain axioms. Real world actions are typically difficult to categorize consistently and are highly context dependent. The approach presented here takes as input a procedural model of the agents environment and produces as output a set of action descriptions that capture how the agent can exploit the dynamics in the environment. An agent constructed with this approach can utilize context sensitive actions, “servo” style actions, and other intuitively efficient ways to manipulate its environment. A process-algebra based representation, RS , is introduced to model the environment and the agents reactions. The paper demonstrates how to analyze an RS environment model so as to automatically generate a set of potentially useful dynamics and convert these to action descriptions. The output action descriptions are designed to be input to an Interval Temporal Logic based planner. A series of examples of reaction construction drawn from the kitting robot domain is worked through, and the prototype implementation of the approach described.

Stable grasping with a multi-fingered robot hand: a behavior-based approach

The paper describes the software design approach and implementation of a stable grasp strategy to control a multifingered robot hand in a dynamic and uncertain environment. The overall design starts with a reactive system, called the Grasp Reactor, which measures the environment, and produces actions based on the environmental situations present. To improve the robustness of the Grasp Reactor, it is augmented with a deliberative component which executes concurrently with the Grasp Reactor. This component, called the Grasp Advisor, communicates global constraints to the Grasp Reactor to improve its decision making capability.

Planning by adaptation: experimental results

A robot system operating in an environment in which there is uncertainty and change needs to combine the ability to react with the ability to plan ahead. In a previous paper we proposed a solution to the problems of integrating planning and reaction: cast planning as adaptation of a reactive system, the planner-reactor approach. In this paper, we present our first experimental results from this approach. The results indicate that the planner-reactor approach is an attractive option for integrating planning and reaction in a robot system, allowing smooth, online updates to the behavior, of a reactive system with little time and behavior penalties accruing from the use of a planner.<<ETX>>

A METHODOLOGY FOR CREATING AND ADAPTING REACTIVE SYSTEMS

This article describes a methodology for building integtated planning-reacting systems. The work is based on a formal approach to building the reactive component (the reactor); this allows us to formalize the concept of a planner improving a reactive system. Our novel planner design emphasizes how the planner can use the reactor to focus its reasoning, as well as how the reactor is guided by the planner to improve its behavior. The reactive component (the reactor) uses a process-based model of robot computation, the RS model. This gives us a powerful representation for actions with precise formal semantics. The duty of the planning component (the planner) is to adapt the reactor to suit a set of objectives and the possibilities afforded by the environment. Planner and reactor both operate continually, separately, and in a complementary fashion. The approach is illustrated with a kitting robot domain problem.

Implementing an integrated approach to planning and reaction

The ability to act flexibly in an uncertain and dynamic environment is one of the key objectives of robotics. In previous work, we described an approach to this problem which we called the planner-reactor approach. This paper reviews that approach and presents our current implementation in depth.

Using perception to plan incremental adaptations

A robot system operating in an environment in which there is uncertainty and change needs to combine the ability to react with the ability to plan ahead. In a previous paper we proposed a solution to the problems of integrating planning and reaction: cast planning as adaptation of a reactive system. The planner, asynchronously tuning the reactor, decides on the appropriate parts of the reactor to be modified based on perceptions: information gathered in the reactor for the express purpose to inform the planner. In this paper, we show the benefits of using perception to adapt the reactor where it needs to be updated most and present our first experimental results from the planner-reactor architecture.<<ETX>>