Behavior-based Robot Research Articles

Visual routines for eye location using learning and evolution

Eye location is used as a test bed for developing navigation routines implemented as visual routines within the framework of adaptive behavior-based AI. The adaptive eye location approach seeks first where salient objects are, and then what their identity is. Specifically, eye location involves: 1) the derivation of the saliency attention map, and 2) the possible classification of salient locations as eve regions. The saliency (where) map is derived using a consensus between navigation routines encoded as finite-state automata exploring the facial landscape and evolved using genetic algorithms (GAs). The classification (what) stage is concerned with the optimal selection of features, and the derivation of decision trees, using GAs, to possibly classify salient locations as eyes. The experimental results, using facial image data, show the feasibility of our method, and suggest a novel approach for the adaptive development of task-driven active perception and navigational mechanisms.

Cambrian intelligence: the early history of the new AI

Until the mid-1980s, AI researchers assumed that an intelligent system doing high-level reasoning was necessary for the coupling of perception and action. In this traditional model, cognition mediates between perception and plans of action. Realizing that this core AI, as it was known, was illusory, Rodney A. Brooks turned the field of AI on its head by introducing the behavior-based approach to robotics. The cornerstone of behavior-based robotics is the realization that the coupling of perception and action gives rise to all the power of intelligence and that cognition is only in the eye of an observer. Behavior-based robotics has been the basis of successful applications in entertainment, service industries, agriculture, mining, and the home. It has given rise to both autonomous mobile robots and more recent humanoid robots such as Brooks' Cog. This book represents Brooks' initial formulation of and contributions to the development of the behavior-based approach to robotics. It presents all of the key philosophical and technical ideas that put this bottom-up approach at the forefront of current research in not only AI but all of cognitive science.

Design patterns for behavior-based robotics

The use of patterns allows proven design expertise to be captured, communicated and reused in areas such as architecture and object-oriented programming. The artificial intelligence discipline of behavior-based robotics is an area in which such design expertise has undoubtedly accumulated, although it has not been captured and formalized. Consequently, the creation of these robotic systems remains a relatively unstructured process requiring a large amount of trial-and-error development. There is therefore a strong case for representing the previous design expertise which has been developed in this field. This paper describes how this may be achieved through the use of design patterns.

Cooperation without deliberation: A minimal behavior-based approach to multi-robot teams

While terminology and some concepts of behavior-based robotics have become widespread, the central ideas are often lost as researchers try to scale behavior to higher levels of complexity. “Hybrid systems” with model-based strategies that plan in terms of behaviors rather than simple actions have become common for higher-level behavior. We claim that a strict behavior-based approach can scale to higher levels of complexity than many robotics researchers assume, and that the resulting systems are in many cases more efficient and robust than those that rely on “classical AI” deliberative approaches. Our focus is on systems of cooperative autonomous robots in dynamic environments. We will discuss both claims that deliberation and explicit communication are necessary to cooperation and systems that cooperate only through environmental interaction. In this context we introduce three design principles for complex cooperative behavior— minimalism, statelessness and tolerance—and present a RoboCup soccer system that matches the sophistication of many deliberative soccer systems while exceeding their robustness, through the use of strict behavior-based techniques with no explicit communication.

Behavior-based robotics

Whence behaviour? animal behaviour robot behaviour behaviour-based architectures representational issues for behavioural systems hybrid deliberative/rective architectures perceptual basis for behaviour-based control adaptive behaviour social behaviour fringe robotics - beyond behaviour.

Behavior-based artificial intelligence in miniature mobile robot

An intelligent mobile robot that implements concepts of mechatronics, mobile robotics, and behavior-based artificial intelligence has been developed. The design process for such a mobile robot employs a concept of simple trade-off between the mechanical, electrical, and computational systems for the benefit of improving the robots overall system performance. The main features of the robot, named SCAVENGER, are to navigate freely in a controlled environment, search for one or more target objects with known characteristics (colored golf balls), and avoid the other objects as obstacles. A combination of infrared sensors, color detectors, and bumper skirts is used to facilitate navigation and object recognition. The robots brain consists of an on-board MC68HC11 microcontroller, which is programmed in C. The control software implements behavior-based artificial intelligence, where the robots overall intelligence is made up of layers of several simple and primitive behaviors similar to those observed in insects. In this work, the design of the robots subsystems, where the implementation of mechatronics is most in effect, is covered in more detail with a particular interest in object recognition and collection systems.

AUTONOMOUS AGENTS IN USER INTERFACES

In this paper we present novel concepts for user interfaces based on agents actively exploring their environment, including the user's behavior. These autonomous agents are active entities in a hypermedia artwork on the development and artistic challenge of three-dimensional sculpturing on video screens and with laser installations. Such an approach challenges current paradigms of user interfaces in a fundamental way. Each individual, users and agents alike, is forced to construct their own concepts and strategies to operate successfully, based on their experiences. The strategies of the agent cannot be conveyed explicitly. Instead, they emerge from concepts of the domain, concepts of the users, the agent's own drives, and the interactions between users and the agent; all together they form the main source for the generation of new concepts and strategies. The history of behavior-based robotics provides us with similar evidence that active exploration in particular as opposed to mere deliberation constitutes a necessary step in improving a system's performance. While this approach paves the path for novel ways of interacting with computers, currently there is still a lack of theories and methodologies to help in the development of such interfaces. The constructivistic point-of-view Papert, 1993; Kafai & Resnick, 1996 can contribute some theoretical background, while paying attention to the novel abilities of computer-based multimedia systems. We regard concepts from second-order cybernetics and new system science metaphors to be a valuable and successful methodology for this work.

An intelligent robotic system based on a fuzzy approach

This paper deals with a fuzzy-based intelligent robotic system that requires various capabilities normally associated with It acquires skills and knowledge through interaction with a dynamic environment. Subsumption architectures, behavior-based artificial intelligence, and behavioral engineering for robotic systems have been discussed as new technologies for intelligent robotic systems. This paper proposes a robotic system with structured intelligence. We focus on a mobile robotic system with a fuzzy controller and propose a sensory network that allows the robot to perceive its environment. An evolutionary approach improves the robot's performance. Furthermore, we discuss the effectiveness of the proposed method through computer simulations of collision avoidance and path-planning problems.

Navigation for a Behavior-Based Autonomous Mobile Robot

We propose an implemented architecture that integrates possession of environmental knowledge into a reactive, behavior-based autonomous robot. As an alternative to hybrid combinations of behavior- and model-based systems, we present a fully behavior-based system that takes reactive, deliberate behavior. To get knowledge, a robot makes a the World Image, gradually constructed through experience as higher animals with self-organization do, without symbolic representation in associative memory. Since our World Image changes in direct relation to behavior, a robot navigates adaptively and reactively to its destination following a path plan based on the World Image of a dynamic environment. The path plan a robot makes is not a sequence of routes as in conventional planning, but flexible as in reactive planning. If a robot detects a failure, it reaches its destination without replanning or error recovery. Our approach makes practical a behavior-based robot already adapted to the real world.

Shaping: The Link Between Rats and Robots

The term shaping applies to a family of procedures which were originally developed in animal learning as methods of producing new and sometimes elaborate forms of behavior. The success of operant shaping methods in animal and abnormal psychology has attracted the attention of some roboticists who view these procedures as a means of generating adaptive behavior in both simulated and real robots. This paper outlines the two principal forms of shaping and looks at some of the associative and motivational influences which determine the course and outcome of shaping methods in animals. A number of recent robotic models which incorporate operant shaping concepts are then reviewed in terms of their eff ectiveness; the conclusion from this analysis is that, while some progress has been made, none of these models represent a satisfactory application of animal shaping procedures. It is clear that an effective implementation of shaping methods in behaviorbased robotics requires both a better understanding of the nature and course of shaping in real animals and the development, by roboticists and others, of more sophisticated models which can exploit this improved understanding.

Evolutionary construction of an immune network-based behavior arbitration mechanism for autonomous mobile robots

In recent years, in the field of artificial intelligence (AI), much attention has been focused on reactive planning approaches such as behavior-based AI and new AI. However, a criticism of these approaches is that their arbitration among competence modules is fixed against dynamically changing environments. On the other hand, biological information processing systems have various interesting characteristics from an engineering viewpoint. Among them, the immune system plays an important role in maintaining its own system against dynamically changing environments. Based on this fact, we have investigated a new decentralized consensus-making system for the behavior arbitration of autonomous mobile robots, inspired by the idiotypic network hypothesis in immunology. In this paper, we illustrate the usefulness of our proposed method by means of computer simulations and experiments. © 1998 Scripta Technica. Electr Eng Jpn, 123(3): 1–10, 1998

Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior

Work in behavior-based systems focuses on functional modeling, that is, the synthesis of life-like and/or biologically inspired behavior that is robust, repeatable and adaptive. Inspiration from cognitive science, neuroscience and biology drives the development of new methods and models in behavior-based robotics, and the results tie together several related fields including artificial life, evolutionary computation, and multi-agent systems. Ideas from artificial intelligence and engineering continue to be explored actively and applied to behavior-based robots as their role in animal modeling and practical applications is being developed.

Animats: computer-simulated animals in behavioral research.

The term animat refers to a class of simulated animals. This article is intended as a nontechnical introduction to animat research. Animats can be robots interacting with the real world or computer simulations. In this article, the use of computer-generated animats is emphasized. The scientific use of animats has been pioneered by artificial intelligence and artificial life researchers. Behavior-based artificial intelligence uses animats capable of autonomous and adaptive activity as conceptual tools in the design of usefully intelligent systems. Artificial life proponents view some human artifacts, including informational structures that show adaptive behavior and self-replication, as animats may do, as analogous to biological organisms. Animat simulations may be used for rapid and inexpensive evaluation of new livestock environments or management techniques. The animat approach is a powerful heuristic for understanding the mechanisms that underlie behavior. The simple rules and capabilities of animat models generate emergent and sometimes unpredictable behavior. Adaptive variability in animat behavior may be exploited using artificial neural networks. These have computational properties similar to natural neurons and are capable of learning. Artificial neural networks can control behavior at all levels of an animat's functional organization. Improving the performance of animats often requires genetic programming. Genetic algorithms are computer programs that are capable of self-replication, simulating biological reproduction. Animats may thus evolve over generations. Selective forces may be provided by a human overseer or be part of the simulated environment. Animat techniques allow researchers to culture behavior outside the organism that usually produces it. This approach could contribute new insights in theoretical ethology on questions including the origins of social behavior and cooperation, adaptation, and the emergent nature of complex behavior. Animat studies applied to domestic animals have been few so far, and have involved simulations of space use by swine. I suggest other applications, including modeling animal movement during human handling and the effects of environmental enrichment on the satisfaction of behavioral needs. Appropriate use of animat models in a research program could result in savings of time and numbers of animals required. This approach may therefore come to be viewed as both ethically and economically advantageous.

行動規範型自律移動ロボットの世界像獲得およびナビゲーション手法

行動規範型自律移動ロボットの世界像獲得およびナビゲーション手法

The new wave in robot learning

The new wave in robotics research began to blossom in the mid-1980s with the convergence of a number of seemingly diverse research trends. All of these were inspired, to different degrees, by systems and include neuro-computing, behaviorbased robotics, artificial life, evolutionary methods and behavioral modeling. The new wave is, in a sense, situated between the high-level planning robotics of pre-1980s deliberative artificial intelligence (AI) and low-level control theory. It grew out of a drive for simplicity, adaptivity, and an attitude that nature knows best; natural stupiddity rather than Artificial Intelligence. It should be noted, however, there are many different levels of biological plausibility in the robotics literature. They range from general

The brain has a body: adaptive behavior emerges from interactions of nervous system, body and environment

Studies of mechanisms of adaptive behavior generally focus on neurons and circuits. But adaptive behavior also depends on interactions among the nervous system, body and environment: sensory preprocessing and motor post-processing filter inputs to and outputs from the nervous system; co-evolution and co-development of nervous system and periphery create matching and complementarity between them; body structure creates constraints and opportunities for neural control; and continuous feedback between nervous system, body and environment are essential for normal behavior. This broader view of adaptive behavior has been a major underpinning of ecological psychology and has influenced behavior-based robotics. Computational neuroethology, which jointly models neural control and periphery of animals, is a promising methodology for understanding adaptive behavior.

Intelligent control for perceptually attentive agents: The S ∗ proposal

This proposal attempts to reconcile the successful behavior-based robot architectures with the results presented by Tsotsos (1995) which show that mechanisms which were anathema in the early behaviorist dogma, such as attention and goal-directed processing are perhaps necessary if vision is used as a major sensor. S ∗ is a conceptual strategy for control which facilitates these mechanisms. Further, a unique method for determining whether a particular S ∗ behavior collection can satisfy a given mission is described. A language for mission plan specification is defined permitting the integration of deliberative and reactive specifications and a proof procedure is sketched which can determine if a particular behavior set violates the timing and feasibility constraints of a specific mission.

The behavior based firm : Application of recent ai concepts to company management

Artificial Intelligence (AI) has a long tradition in developing technological means for the control of complex systems. This article reviews recent developments in the area of embodied AI and behavior-based robotics and formulates principles as they appear to be applicable in managerial problem domains. We compare these principles to new management concepts such as the horizontal organization and lean production, which exhibit definite similarities to proposals recently made by roboticists. An analysis of these similarities identifies the importance of a tight system-environment coupling. This connection is achieved by a rapid and precise evaluation of external observables from many internal processes. Another important factor is the process orientation of control that marks a clear departure from traditional approaches based on functional decomposition.

A Robot Trace Maker: Modeling the Fossil Evidence of Early Invertebrate Behavior

The study of trace fossils, the fossilized remains of animal behavior, reveals interesting parallels with recent research in behavior-based robotics. This article reports robot simulations of the meandering foraging trails left by early invertebrates that demonstrate that such trails can be generated by mechanisms similar to those used for robot wall-following. We conclude with the suggestion that the capacity for intelligent behavior shown by many behavior-based robots is similar to that of animals of the late Precambrian and early Cambrian periods approximately 530 to 565 million years ago.

Using Emergent Modularity to Develop Control Systems for Mobile Robots

A new way of building control systems, known as behavior-based robotics, has recently been proposed to overcome the difficulties of the traditional artificial intelligence approach to robotics. This new approach is based on the idea of providing the robot with a range of simple behaviors and letting the environment determine which behavior should have control at any given time. We will present a set of experiments in which neural networks with different architectures have been trained to control a mobile robot designed to keep an arena clear by picking up trash objects and releasing them outside the arena. Controller weights are selected using a form of genetic algorithm and do not change during the lifetime (i.e., no learning occurs). We will compare, in simulation and on a real robot, five different network architectures and will show that a network that allows for fine-grained modularity achieves significantly better performance. By comparing the functionality of each network module and its interaction with a description of the simple behavior components, we will show that it is not possible to find simple correlations; rather, module switching and interaction are correlated with low-level sensorimotor mappings. This implies that the engineering-oriented approach to behavior-based robotics might have serious limitations because it is difficult to know in advance the appropriate mappings between behavior components and sensorimotor activity for complex tasks.