We describe an extension of graphplan to a subset of ADL that allows conditional and universally quantified effects in operators in such away that almost all interesting properties of the original graphplan algorithm are preserved.

We present an expressive agent design language for reinforcement learning that allows the user to constrain the policies considered by the learning process.The language includes standard features such as parameterized subroutines, temporary interrupts, aborts, and memory variables, but also allows for unspecified choices in the agent program. For learning that which isn’t specified, we present provably convergent learning algorithms. We demonstrate by example that agent programs written in the language are concise as well as modular. This facilitates state abstraction and the transferability of learned skills. 1

its performance. In fact, we use the expression robot shaping to denote the use of learning as a means to translate suggestions coming from an external trainer into an effective control strategy that allows a robot to achieve a goal. We borrowed the term shaping from experimental psychology (Skinner, 1938), because training an artificial robot somewhat resembles what experimental psychologists do in their laboratories, when they train an experimental subject to produce a predefined response. The important point, which differentiates our approach from most current research on learning autonomous agents, is that the trainer plays a fundamental role in the learning process: most of the book is aimed at showing how to use a trainer to develop control systems for simulated and real robots. We also use the term behavior engineering to characterize a new technological discipline, the objective of which is to provide techniques, methodologies and t

Despite many decades of research into mobile robot control, reliable, high-speed motion in complicated, uncertain environments remains an unachieved goal. In this paper we present a solution to realtime motion control that can competently maneuver a robot at optimal speed even as it explores a new region or encounters new obstacles. The method uses a navigation function to generate a gradient field that represents the optimal (lowest-cost) path to the goal at every point in the workspace. Additionally, we present an integrated sensor fusion system that allows incremental construction of an unknown or uncertain environment. Under modest assumptions, the robot is guaranteed to get

In the behavior-based approach the control of a robot is shared between multiple behaviors with different and possibly incommensurable objectives. In most cases when deciding what next action to take, multiple conflicting objectives should be considered simultaneously. Thus one faces the problem of deciding what next action to select. This is known as the action selection problem and is the primary focus of this dissertation. In particular, two aspects of the action selection problem, that are subject to investigation consist of 1) the formulation of effective mechanisms for coordination of the behaviors' activities into strategies for rational and coherent behavior and 2) the construction of fault-tolerant behaviors from a multitude of less reliable ones. Regarding the first issue, it is demonstrated that multiple objective decision theory provides a suitable formalism to encompass ideas from behavior-based system synthesis and control, where each behavior is cast as an objective fun...

In behavior-based robotics the control of a robot is shared between a set of purposive perception-action units, called behaviors. Based on selective sensory information, each behavior produces immediate reactions to control the robot with respect to a particular objective, i.e., a narrow aspect of the robot's overall task such as obstacle avoidance or wall following. Behaviors with di erent and possibly incommensurable objectives may produce con icting actions that are seemingly irreconcilable. Thus a major issue in the design of behavior-based control systems is the formulation of e ective mechanisms for coordination of the behaviors' activities into strategies for rational and coherent behavior. This is known as the action selection problem (also refereed to as the behavior coordination problem) and is the primary focus of this overview paper. Numerous action selection mechanisms have been proposed over the last decade and the main objective of this document istogive a qualitative overview of these approaches. 2 1

Effective task-level control is critical for robots that are to engage in purposeful activity in realworld environments. This paper describes PRSLite, a task-level controller grounded in a procedural knowledge approach to action description. The controller embodies much of the philosophy that underlies the Procedural Reasoning System (PRS) but in a minimalist fashion. Several features of PRS-Lite distinguish it from its predecessor, including a richer goal semantics and a generalized control regime. Both of these features are critical for supporting the management of continuous processes employed in current-generation robots. PRS-Lite has been used extensively as a task-level controller for a robot whose underlying behaviors are implemented as fuzzy rules. Tasks to which it has been applied include vision-based tracking, autonomous exploration, and complex delivery scenarios. Introduction In recent years, there has been a convergence of design methodologies for certain aspects of rob...

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The problem of action selection by autonomous agents becomes increasingly difficult when acting in continuous, non-deterministic and dynamic environments pursuing multiple and possibly conflicting goals. We propose a method that exploits additional information gained from continuous states, is able to deal with unexpected situations, and takes multiple and conflicting goals into account including additional motivational aspects such as dynamic goals, which allow for situation-dependent motivational influence on the agent. Further we show some domain independent properties of this algorithm along with empirical results gained using the RoboCup simulated soccer environment. 1

Abstract—Exploration of high risk terrain areas such as cliff faces and site construction operations by autonomous robotic systems on Mars requires a control architecture that is able to autonomously adapt to uncertainties in knowledge of the environment. We report on the development of a software/hardware framework for cooperating multiple robots performing such tightly coordinated tasks. This work builds on our earlier research into autonomous planetary rovers and robot arms. Here, we seek to closely coordinate the mobility and manipulation of multiple robots to perform examples of a cliff traverse for science data acquisition, and site construction operations including grasping, hoisting, and transport of extended objects such as large array sensors over natural, unpredictable terrain. In support of this work we have developed an enabling distributed control architecture called control architecture for multirobot planetary outposts (CAMPOUT) wherein integrated multirobot mobility and control mechanisms are derived as group compositions and coordination of more basic behaviors under a task-level multiagent planner. CAMPOUT includes the necessary group behaviors and communication mechanisms for coordinated/cooperative control of heterogeneous robotic platforms. In this paper, we describe CAMPOUT, and its application to ongoing physical experiments with multirobot systems at the Jet Propulsion Laboratory in Pasadena, CA, for exploration of cliff faces and deployment of extended payloads. Index Terms—Distributed control architecture, multiple mobile robots, robot outposts, tight coordination. I.