Showing papers on "Social robot published in 1997"

The uses of fuzzy logic in autonomous robot navigation

Abstract: The development of techniques for autonomous navigation in real-world environments constitutes one of the major trends in the current research on robotics. An important problem in autonomous navigation is the need to cope with the large amount of uncertainty that is inherent of natural environments. Fuzzy logic has features that make it an adequate tool to address this problem. In this paper, we review some of the possible uses of fuzzy logic in the field of autonomous navigation. We focus on four issues: how to design robust behavior-producing modules; how to coordinate the activity of several such modules; how to use data from the sensors; and how to integrate high-level reasoning and low-level execution. For each issue, we review some of the proposals in the literature, and discuss the pros and cons of fuzzy logic solutions.

Virtual model control of a bipedal walking robot

Abstract: The transformation from high level task specification to low level motion control is a fundamental issue in sensorimotor control in animals and robots. This paper describes a control scheme called virtual model control that addresses this issue. Virtual model control is a motion control language that uses simulations of imagined mechanical components to create forces, which are applied through real joint torques, thereby creating the illusion that the virtual components are connected to the robot. Due to the intuitive nature of this technique, designing a virtual model controller requires the same skills as designing the mechanism itself. A high level control system can be cascaded with the low level virtual model controller to modulate the parameters of the virtual mechanisms. Discrete commands from the high level controller would then result in fluid motion. Virtual model control has been applied to a physical bipedal walking robot. A simple algorithm utilizing a simple set of virtual components has successfully compelled the robot to walk continuously over level terrain.

Robot Shaping: An Experiment in Behavior Engineering

Abstract: From the Publisher: foreword by Lashon Booker "[This] book gives a clear and comprehensive exposition of [the authors] extensive experience in integrating reinforcement learning and autonomous robotics. Their continuing contribution is to the development of a distinct engineering discipline (`Behavior engineering') through which such robots can be created. I am excited because their efforts combine some of the best theoretical ideas with a strong eye for the practical - for what will actually work." -- Stewart W. Wilson, The Rowland Institute for Science To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned. Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy," and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.

Decentralized motion planning for multiple mobile robots: the cocktail party model

Abstract: This paper presents an approach for decentralized real-time motion planning for multiple mobile robots operating in a common 2-dimensional environment with unknown stationary obstacles. In our model, a robot can see (sense) the surrounding objects. It knows its current and its target‘s position, is able to distinguish a robot from an obstacle, and can assess the instantaneous motion of another robot. Other than this, a robot has no knowledge about the scene or of the paths and objectives of other robots. There is no mutual communication among the robots; no constraints are imposed on the paths or shapes of robots and obstacles. Each robot plans its path toward its target dynamically, based on its current position and the sensory feedback; only the translation component is considered for the planning purposes. With this model, it is clear that no provable motion planning strategy can be designed (a simple example with a dead-lock is discussed); this naturally points to heuristic algorithms. The suggested strategy is based on maze-searching techniques. Computer simulation results are provided that demonstrate good performance and a remarkable robustness of the algorithm (meaning by this a virtual impossibility to create a dead-lock in a “random” scene).

Robot system and robot control device

Abstract: An object of the device according to the present invention is to enable small-volume, high-diversity production to be conducted efficiently, in the same way as in the conventional FMS, whilst at the same time overcoming problems associated with the conventional techniques, such as the complexity of creating programs for manufacturing processes, work interruptions due to robot breakdown, and `deadlock` incidents arising during operation. When seed data indicating an objective task (for example, the task of completing a particular product) is input to a computer, by communicating with hardware robots via a communications network, the computer selects a plurality of hardware robots, which are capable of implementing cooperatively the objective task indicated by the input seed data, and which are not engaged in any task based on other seed data. A software robot is created by inputting the seed data to at least one of the selected plurality of hardware robots. When the plurality of robots have been operated by means of the generated software robot and have completed the objective task, the seed data is withdrawn from the hardware robot(s) to which it was input.

Real time remotely controlled robot

Abstract: A robot system including a robot, an operator control center and having a communication system connecting the robot and control center wherein an operator in the control center responds with natural movements to stimulus signals from the robot environment by issuing commands that control the robot. In one embodiment, the operator occupies a command chair with an exoskeletal arm that is secured to the "shoulder" (backrest) of the chair. The operator slides his hand into a glove of the invention attached to the end of the exoskeletal arm. He views the robot environment through a video screen and exerts control in response to views presented on the screen. The communication system sends signals to the operator glove in response to conditions at the robot site prompting the operator to take appropriate action.

Using emergent modularity to develop control systems for mobile robots

Abstract: 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...

Neural Network Vision for Robot Driving

Abstract: Autonomous navigation is a difficult problem for traditional vision and robotic techniques, primarily because of the noise and variability associated with real world scenes. Autonomous navigation systems based on traditional image processing and pattern recognition techniques often perform well under certain conditions, but have problems with others. Part of the difficulty stems from the fact that the processing performed by these systems remains fixed across various environments.

Socially embedded learning of the office-conversant mobile robot Jijo-2

Abstract: This paper explores a newly developing direction of machine learning called ''socially embedded learning". In this research we have been building an office-conversant mobile robot which autonomously moves around in an office environment, actively gathers information through close interaction wi th this environment including sensing multi-modal data and making dialog with people in the office, and acquires knowledge about the environment wi th which it ultimately becomes conversant. Here our major concerns are in how the close interaction between the learning system and its social environment can help or accelerate the systems learning process, and what kinds of prepared mechanisms are necessary for the emergence of such interactions. The office-conversant robot is a platform on which we implement our ideas and test their feasibility in a real-world setting. An overview of the system is given and two examples of implemented ideas, i.e. dialog-based map acquisition and route acquisition by following, are described in detail.

Applying genetic programming to evolve behavior primitives and arbitrators for mobile robots

Abstract: The behavior-based approach has been successfully applied to designing robot control systems. This paper presents our work, based on evolutionary algorithms, to program behavior-based robots automatically. Instead of hand-coding all the behavior controllers or evolving an entire control system for an overall task, we suggest our approach at the intermediate level: it includes evolving behavior primitives and behavior arbitrators for a mobile robot to achieve the specified tasks. To examine the developed approach, we evolve a control system for a moderately complicated box-pushing task as an example. We first evolved the controllers in a simulation and then transferred them to the Khepera miniature robot. Experimental results show the promise of our approach, and the evolved controllers are transferred to the real robot without loss of performance.

A cooperative multi-agent system and its real time application to robot soccer

Abstract: The soccer robot system consists of multi agents, with highly coordinated operation and movements so as to fulfill specific objectives, even under adverse situation. The coordination of the multi-agents is associated with a lot of supplementary work in advance. The associated issues are the position correction, prevention of communication congestion, local information sensing in addition to the need for imitating the human-like decision making. A control structure for a soccer robot is designed and several behaviors and actions for a soccer robot are proposed. Modified zone defense as a basic strategy and several special strategies for fouls are applied to SOTY and MIRO teams for MIROSOT (Micro-Robot World Cup Soccer Tournament).

Xavier: experience with a layered robot architecture

Abstract: Office delivery robots have to perform many tasks such as picking up and delivering mail or faxes, returning library books, and getting coffee. They have to determine the order in which to visit locations, plan paths to those locations, follow paths reliably, and avoid static and dynamic obstacles in the process. Reliability and efficiency are key issues in the design of such autonomous robot systems. They must deal reliably with noisy sensors and actuators and with incomplete knowledge of the environment. They must also act efficiently, in real time, to deal with dynamic situations. To achieve these objectives, we have developed a robot architecture that is composed of four layers: obstacle avoidance, navigation, path planning, and task planning. The layers are independent, communicating processes that are always active, processing sensory data and status information to update their decisions and actions. A version of our robot architecture has been in nearly daily use in our building since December 1995. As of January 1997, the robot has traveled more than 110 kilometers (65 miles) in service of over 2500 navigation requests that were specified using our World Wide Web interface.

Adaptive navigation of mobile robots with obstacle avoidance

Abstract: A local navigation technique with obstacle avoidance, called adaptive navigation, is proposed for mobile robots in which the dynamics of the robot are taken into consideration. The only information needed about the local environment is the distance between the robot and the obstacles in three specified directions. The navigation law is a first-order differential equation and navigation to the goal and obstacle avoidance are achieved by switching the direction angle of the robot. The effectiveness of the technique is demonstrated by means of simulation examples.

Adaptive formation control for distributed autonomous mobile robot groups

Abstract: We present adaptive formation control for distributed autonomous mobile robot groups, envisioning mobile robots being used in crime deterrence. Our mobile robot group has a task to prevent a trespasser from passing along a way in a surveillance area by a group formation. To control the formation, each robot has a formation vector. In particular, the formation is controllable by the vectors. The group encounters various ways and each way has its own width. Moreover, the number of the robots in the group is changed when some of them are broken. However, the group in our control method can make a specified formation to achieve the task, adapting to variations of such conditions. In other words, we prove an existence of a distributed control method that enables the adaptation of the formation. The validity of our method is verified by computer simulations.

Visual navigation in an open environment without map

Abstract: We describe how a mobile robot controlled only by visual information can retrieve a particular goal location in an open environment. Our model does not need a precise map nor to learn all the possible positions in the environment. The system is a neural architecture inspired from neurobiological studies using the recognition of visual patterns called landmarks. The robot merges this visual information and its azimuth to build a plastic representation of its location. This representation is used to learn the best movement to reach the goal. A simple and fast online learning of a few places located near the goal allows the robot to reach the goal from anywhere in its neighborhood. The system uses only an egocentric representation of the robot environment and presents very high generalization capabilities. We describe an efficient implementation tested on our robot in two real indoor environments. We show the limitations of the model and its possible extensions to create autonomous robots only guided by visual information.

A Prototype Study of an Autonomous Robot Platform for SewerageSystem Maintenance

Abstract: The paper describes KURT, an autonomous robot platform prototype that is able to navigate through a network of sewage pipes. KURT is a six-wheeled vehicle; it has modular, layered hardware and control architectures. Its sensor configuration consists mainly of stationary and one flexible ultrasound transducers and of two inclinometers. In experiments run in a test sewerage system consisting of concrete pipes of 600 mm diameter, it has proven its abilities to travel safely through straight pipes, to recognize different types of pipe crossings, to turn at crossings, and to navigate from a given start point to arbitrary specified goal points.

Artificial emotional creature for human-robot interaction-a new direction for intelligent systems

Abstract: Summary form only given. Recent advances in robotics have been applied to automation in industrial manufacturing, with the primary purpose of optimizing practical systems in terms of such objective measures as accuracy, speed, and cost. This paper introduces the Artificial Emotional Creature project that seeks to explore a different direction that is not so rigidly dependent on such objective measures. The goal of this project is to explore a new area in robotics, with an emphasis on human-robot interaction. There is a large body of evidence that shows the importance of the interaction between humans and animals such as pets. We have been building a pet robot, as an implementation of an artificial emotional creature, with the subjective appearance of "behaviors" that are dependent on internal states, or "emotions", as well as external stimuli from both the physical environment and human beings. Human-robot interaction plays a large role. The pet robot has visual, audio, and tactile sensors. Olfactory sensors will also be available. The paper will describe an algorithm implementing a focus of attention through the integration of those sensors. In particular, object localization has been developed through the integration of vision and audition, using the interaction of a human being with the robot as the training reference.

A development of a new mechanism of an autonomous unicycle

Abstract: We propose a new mechanism of a unicycle. One feature of this robot is the shape of the wheel which is similar to a rugby ball. The other feature is that the body is separated into upper and lower parts. We give the details of a mechanism of the robot, simple controller system, and some results of a performance experiment using the designed controller. We present experimental results that show that the robot can move in a straight line and steer itself by the proposed mechanism.

Traded Control with Autonomous Robots as Mixed Initiative Interaction

Abstract: This paper describes a problem domain that lends itself to mixed initiative interaction. The domain is traded control with an autonomous robot. Traded control is a situation in which a human wants to control a robot during part of a task and the robot is autonomous during other parts of a task. A significant problem in traded control situations is that the robot doesn’t know how the environment has been changed or what parts of the task have been accomplished when the human has been in control. Because of this, errors can occur when the human relinquishes control back to the robot; these errors can cause potentially dangerous situations. Our solution is to use an intelligent software architecture designed for autonomous robot control and modify it to work in concert with human control. Using an architecture designed for autonomy allows us to use the monitoring functions designed to track the actions of the robot to monitor the actions of human agents for the same tasks. The intelligent software architecture includes a mixed initiative planner, an execution monitor, robotic skills and a user interface. This paper describes the problem domain and our initial attempts at defining a software architecture that operates in the domain.

Grounding communication in situated, social robots

Abstract: Reference LSA3-CONF-1997-003 URL: http://www.cs.man.ac.uk/robotics/Conference/timruk.html Record created on 2005-11-16, modified on 2017-05-10

Dynamic Object Capture Using Fast Vision Tracking

Abstract: This article discusses the use of fast (60 frames per second) object tracking using the COGNACHROME VISION SYSTEM, produced by Newton Research Labs. The authors embedded the vision system in a small robot base to tie for first place in the Clean Up the Tennis Court event at the 1996 Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. Of particular interest is that the authors' entry was the only robot capable of using a gripper to capture and pick up the motorized, randomly moving squiggle ball. Other examples of robotic systems using fast vision tracking are also presented, such as a robot arm capable of catching thrown objects and the soccer-playing robot team that won the 1996 Micro Robot World Cup Soccer Tournament in Taejon, Korea.

Realization of Expressive Mobile Robot

Abstract: This paper proposes a novel human symbiosis robot named Expressive Mobile Robot. Through its behavior and movement it successfully realize nonverbal communication to humans. Towards the future aging society human-robot symbiosis is keenly needed. To realize such human-robot symbiosis, familiarity of robot is crucial. The proposed Expressive Mobile Robot produces familiar impression to humans through its behavior, action and motion. It does no real task other than the expression. Based upon consideration of human gestures, a prototype of the Expressive Mobile Robot has been designed and implemented. It is puppy-sized and has 7 DOF with a head, two arms and a body. Utilizing the prototype robot, several psychological experiments are conducted. Experimental results make it clear that there are proper speed pattern, distance to human and poses for familiar impression. The results support the feasibility of the proposed idea and robot.

The Cog Project

Abstract: We are building a humanoid robot at the MIT Artificial Intelligence Laboratory. It is a legless human sized robot and is meant to be able to emulate human functionality. We are particularly interested in using it as vehicle to understand how humans work, by trying to combine many theories from artificial intelligence, cognitive science, physiology and neuroscience. While undergoing continual revisions in its hardware and software the robot has been operational in one form or another for over three years. We are working on systems that emulate both human development and human social interactions.

Learning to coordinate controllers-reinforcement learning on a control basis

Abstract: Autonomous robot systems operating in an uncertain environment have to be reactive and adaptive in order to cope with changing environment conditions and task requirements. To achieve this, the hybrid control architecture presented in this paper uses reinforcement learning on top of a Discrete Event Dynamic System (DEDS) framework to learn to supervise a set of basis controllers in order to achieve a given task. The use of an abstract system model in the automatically derived supervisor reduces the complexity of the learning problem. In addition, safety constraints may be imposed a priori, such that the system learns on-line in a single trial without the need for an outside teacher. To demonstrate the applicability of the approach, the architecture is used to learn a turning gait on a four legged robot platform.

Perception for a transport robot in public environment

Abstract: FIRST is a mobile robotic system suited to the transport within hospitals of heavy trolleys such as those containing laundry, meal trays, drugs, etc. The system comprises a fleet of mobile robots, a central control station and several distributed control stations. The robots may use any route following a network of paths taught during a teleoperation phase. They navigate autonomously in the environment of the hospital, in particular in the presence of humans, be they hospital staff, patients or visitors, all in complete safety. Autonomous navigation requires the ability to react to unexpected events, hence the importance of a perceptual system which has two functions to recognise obstacles in order to avoid them in a safe and autonomous fashion, and to detect all situations which could endanger people and goods during the transport and handling of the trolleys. This paper presents the design principles and the performance of the perception system observed during site trials.

Motion planning of a pneumatic robot using a neural network

Abstract: Integration of sensing and motion planning plays a crucial role in autonomous robot operation. We present a framework for sensor-based robot motion planning that uses learning to handle arbitrarily configured sensors and robots. The theoretical basis of this approach is the concept of the perceptual control manifold that extends the notion of the robot configuration space to include sensor space. To overcome modeling uncertainty, the topology-representing-network algorithm is employed to learn a representation of the perceptual control manifold. By exploiting the topology-preserving features of the neural network, a diffusion-based path planning strategy leads to flexible obstacle avoidance. The practical feasibility of the approach is demonstrated on a pneumatically driven robot arm (SoftArm) using visual sensing.

Humanoid as a research vehicle into flexible complex interaction

Abstract: This paper considers the humanoid research as an approach to understanding and realizing complex real world interactions among the robot, environment, and human. As a first step towards extracting a common principle over the three term interactions, the concept of action oriented control has been investigated with simulation example. The complex interaction view casts unique constraints on the design of a humanoid, such as the whole body, smooth shape and non-functional-modular design. A brief description of ongoing design of ETL-humanoid which conforms to the above constraints is presented.

Humanoid robot-development of an information assistant robot Hadaly

Abstract: This paper describes a humanoid robot "Hadaly" that was developed as a basic model for the final version of Humanoid. In this study, the authors feature an attempt to find the configurations and functions that are required for a humanoid. Hadaly consists of four systems; an audio-visual system, a head system, a speech system and an arm system. The configurations and functions required for a humanoid robot are clarified based on the results of the information assistance experiment with Hadaly.

Evolving sufficient robot controllers

Abstract: Different methods exist for reducing the time consumption in evolutionary robotics experiments. One is to use simulations, while another is to evolve controllers that are no more complex than task fulfilment requires. Behaviors such as exploration and homing, that seemingly demand a complex control system, only require a perceptron that connects a robot's sensors to its motors. This is shown by evolving such neurocontrollers for the Khepera robot. An exploitation of the robot's perception of the environment's geometrical shape allows the robot to encode time, even though explicitly it is not presented with the time and there are no recurrent connections in the neurocontroller.