Primitive Behaviors Research Articles

IJARCCE - Computer and Communication Engineering

Reactive robots provide more rapid and flexible response than is attainable through traditional methods of robotic control. The subsumption based architecture is reactive control system which it is also referred to as behaviour- based robots. They are instructed to perform through the activation of collection of low-level primitive behaviours. For real time obstacle avoiding robots, a reactive control system is most suitable as it tightly couples perception therefore in this paper design and implementation of subsumption based reactive control system is presented.

MOTIVASI MASYARAKAT DESA TELAGA SILABA KECAMATAN AMUNTAI SELATAN TERHADAP PENGOBATAN BACABUT

This paper describes the phenomenon of mysticism outside of religion, namely ‘Bacabut’ treatment. Named ‘Bacabut’ treatment because this treatment can remove the disease from the body in the form of objects such as gravel, broken glass, leather of walnut, ironwood, or ‘Sagar’ (palm stick). The treatment is performed by a physic who gains strength / magic that comes from the help of supernatural beings in the spirit world which is called ‘friend’ (gampiran). In the middle of the progress of society in every field, the understanding and primitive behavior sometimes still influence their lives. People’s dependence on primitive belief and the power of supernatural mystical and magical, making they are sick to prefer going to psychic / shaman to cure the illness than going to a doctor or a health worker. Psychologically, their motivation for treatment to the ‘Bacabut’ treatment divided to two kinds of intrinsic and extrinsic motivation. The intrinsic motivation consists of their own accord and they would like to know / to try it. There are some extrinsic motivations, such as families’ suggestion, many people who come there for treatment, be sure by seeing the evidence. It’s simple, close and it does not specify rates/cheap.

A Simple Scheme for Formation Control Based on Weighted Behavior Learning

Several correlated issues of autonomy and simplicity regarding formation control for robots with a self-awareness mechanism in unstructured environments are considered. To achieve autonomy and simplicity, a hybrid scheme is derived for robot maneuvering based on a multibehavioral system. The system holds some self-awareness capabilities ensuring precision and robustness in the presence of internal and external disturbances within the limited capacity of interrobot communication. This is to ensure that the robots can march and simultaneously maintain their assigned formation and avoid hazardous collisions on the way to their destination. These self-awareness capabilities are achieved through a layered reinforcement learning algorithm. At the bottom level, robots are equipped with a set of primitive behaviors learned prior to team formation. The high-level combined behavior is generated by multiplying the outputs of each primitive behavior by its weight, and then summing and normalizing the results. The weights keep adaptively adjusting to the proposed reinforcement learning method. Once a robot receives a command providing the formation shape and the location of the destination, the robot approaches the destination autonomously and keeps an appropriate distance from its neighbors to maintain the assigned pattern. Leadership was given to a robot occupying the lead position. The volunteer leader takes the responsibility for keeping the formation, reporting its existence to the other robots, and resetting the position assignment after passing obstacles. Simulations show the practicality and performance of the proposed approach in both static and dynamic obstacle environments.

A Hierarchical Fuzzy Control Design for Indoor Mobile Robot

This paper presents a motion control for an autonomous robot navigation using fuzzy logic motion control and stereo vision based path-planning module. This requires the capability to maneuver in a complex unknown environment. The mobile robot uses intuitive fuzzy rules and is expected to reach a specific target or follow a prespecified trajectory while moving among unforeseen obstacles. The robot's mission depends on the choice of the task. In this paper, behavioral-based control architecture is adopted, and each local navigational task is analyzed in terms of primitive behaviors. Our approach is systematic and original in the sense that some of the fuzzy rules are not triggered in face of critical situations for which the stereo vision camera can intervene to unblock the mobile robot.

A GOAL-ORIENTATION FRAMEWORK FOR SELF-ORGANIZING CONTROL

Self-organization, especially in the framework of embodiment in biologically inspired robots, allows the acquisition of behavioral primitives by autonomous robots themselves. However, it is an open question how self-organization of basic motor primitives and goal-orientation can be combined, which is a prerequisite for the usefulness of such systems. In the paper at hand we propose a goal-orientation framework allowing the combination of self-organization and goal-orientation for the control of autonomous robots in a mutually independent fashion. Self-organization based motor primitives are employed to achieve a given goal. This requires less initial knowledge about the properties of robot and environment and increases adaptivity of the overall system. A combination of self-organization and reward-based learning seems thus a promising route for the development of adaptive learning systems.

Extraction of primitive representation from captured human movements and measured ground reaction force to generate physically consistent imitated behaviors

In this paper, we propose an imitation learning framework to generate physically consistent behaviors by estimating the ground reaction force from captured human behaviors. In the proposed framework, we first extract behavioral primitives, which are represented by linear dynamical models, from captured human movements and measured ground reaction force by using the Gaussian mixture of linear dynamical models. Therefore, our method has small dependence on classification criteria defined by an experimenter. By switching primitives with different combinations while estimating the ground reaction force, different physically consistent behaviors can be generated. We apply the proposed method to a four-link robot model to generate squat motion sequences. The four-link robot model successfully generated the squat movements by using our imitation learning framework. To show generalization performance, we also apply the proposed method to robot models that have different torso weights and lengths from a human demonstrator and evaluate the control performances. In addition, we show that the robot model is able to recognize and imitate demonstrator movements even when the observed movements are deviated from the movements that are used to construct the primitives. For further evaluation in higher-dimensional state space, we apply the proposed method to a seven-link robot model. The seven-link robot model was able to generate squat-and-sway motions by using the proposed framework.

Simplifying Translation Validation via Model Extrapolation

We revisit our case study on the NASA's Voyager space mission to automatically discover its behaviour by means of model transformation and automata learning. We investigate the conformance of three structurally different types of specification of the case study: (1) a formal specification given in ASSL, (2) a derived implementation in Java, and (3) two behavioral models, one derived from the ASSL specification and one learned from the Java implementation. This way we show that Behavioural Mining, that extracts directly analyzable behavioural models from other artifacts (specifications or code) is a practicable and very simple way to obtain a process-oriented description of third-party systems. As the learning technique can be tailored to different abstraction levels according what behavioural primitives we decide to observe, we show and discuss different alternative learned models. This process oriented description is directly amenable to formal verification, as we show here by means of model checking.

Neuromorphic Visual Information Processing –Bio-inspired Vision

 Abstract—This paper describes the early vision of bio-inspired neuromorphic system, mimicking the primitive behaviour of visual cortex. The proposed bio-inspired vision exhibits the biologically plausible function of mimicking the cat's visual cortex experimentation of Hubel and Wiesel. The neuromorphic implementation of vision is inspired by the directional visual signal selectivity of cortex and the CMOS spiking neuron based on Hodgkin-Huxley formalism. The feasibility of neuromorphic early vision is demonstrated for applications of the transportation safety system of passengers' occupancy detection of 95%, based on the recognition of human head figures.

Compositionality in neural control: an interdisciplinary study of scribbling movements in primates

This article discusses the compositional structure of hand movements by analyzing and modeling neural and behavioral data obtained from experiments where a monkey (Macaca fascicularis) performed scribbling movements induced by a search task. Using geometrically based approaches to movement segmentation, it is shown that the hand trajectories are composed of elementary segments that are primarily parabolic in shape. The segments could be categorized into a small number of classes on the basis of decreasing intra-class variance over the course of training. A separate classification of the neural data employing a hidden Markov model showed a coincidence of the neural states with the behavioral categories. An additional analysis of both types of data by a data mining method provided evidence that the neural activity patterns underlying the behavioral primitives were formed by sets of specific and precise spike patterns. A geometric description of the movement trajectories, together with precise neural timing data indicates a compositional variant of a realistic synfire chain model. This model reproduces the typical shapes and temporal properties of the trajectories; hence the structure and composition of the primitives may reflect meaningful behavior.

A behaviour sequencing and composition architecture based on ontologies for entertainment humanoid robots

The research in Human–Robot interaction stresses the importance of non verbal behaviours in order to enforce credibility and believability in robots. When using small and cheap entertainment robots, verbal cues are very limited, gaze and facial gestures can be absent, whereas body gestures comprise–as a matter of fact–only stereotyped behaviours. The main contribution of this article is an evaluation study about the effects of pure body gestures when entertainment robots are observed by humans in an interaction process. Robots are required to execute a sequence of behaviours, as if they were playing on a stage. To this aim, a model for composing behaviour primitives for entertainment humanoid robots is presented. Both single and multi robot scenarios are considered. The framework can sequence robot behaviours using complex scripts, which can be easily programmed using standard ontology-based knowledge representation mechanisms. Procedures are used to operate on the ontology representation in order to execute behaviours in a fully hierarchical and recursive fashion. Experimental validation with an interview campaign has been carried out using toy Kondo robots.

패턴 인식 알고리즘 기반 휴머노이드 경로 시스템 개발

본 논문에서는 패턴 인식 알고리즘을 기반으로 인간 형태를 가진 휴머노이드 로봇의 보행 동작을 제어하는 경로 인식 시스템을 개발하였다. 휴머노이드 로봇이 효과적인 작업 수행을 할 수 있도록 행동 프리미티브를 정의 하였으며, Canny 에지 검출 알고리즘을 적용한 보도 블록의 패턴 및 색상 추출, 이를 기반으로 한 이동 방향을 인식하는 알고리즘 제안하고, 리눅스 운영체제와 영상 카메라가 장착된 소형 휴머노이드 임베디드 시스템에 구현하였다. 제안 알고리즘의 성능 실험을 휴머노이드 로봇의 동작 속도 및 인식율에 관점에서 수행하였으며, 다양한 현실 환경을 반영하기 위해 경사도 및 조도 변화를 적용하였다. 실험 결과 제안 알고리즘은 다양한 환경에서 시각 장애인의 길안내 도우미 로봇으로서 적절한 수준에서 반응함을 확인하였다. In this paper, we develop a pattern recognition algorithm applied to a humanoid robot which is exploited as a guide for visually handicapped persons to find a desired path to their destinations. Behavior primitives of a humanoid robot are defined, and Canny's edge detection algorithm is employed to extract the pattern and color of the paving blocks that especially devised for visually handicapped persons. Based on these, an efficient path finding algorithm is developed and implemented on a humanoid robot, running on an embedded linux operating system equipped with a video camera. The performance of our algorithm is experimentally examined in terms of the response time and the pattern recognition ratio. In order to validate our algorithm in various realistic environments, the experiments are repeatedly performed by changing the tilt of paving blocks and the brightness in surrounding area. The results show that our algorithm performs sufficiently well to be exploited as a path finding system for visually handicapped persons.

Fusion of Hierarchical Behavior-based Actions in Mobile Robot Using Fuzzy Logic

This paper presents mobile robot control architecture of hierarchical behaviors, inspired by biological life. The system is reactive, highly parallel, and does not rely on representation of the environment. The behaviors of the system are designed hierarchically from the bottom-up with priority given to primitive behaviors to ensure the survivability of the robot and provide robustness to failures in higher-level behaviors. Fuzzy logic is used to perform command fusion on each behavior's output. Simulations of the proposed methodology are shown and discussed. The simulation results indicate that complex tasks can be performed by a combination of a few simple behaviors and a set of fuzzy inference rules.

Multiple Action Sequence Learning and Automatic Generation for a Humanoid Robot Using RNNPB and Reinforcement Learning

This paper proposes how to learn and generate multiple action sequences of a humanoid robot. At first, all the basic action sequences, also called primitive behaviors, are learned by a recurrent neural network with parametric bias (RNNPB) and the value of the internal nodes which are parametric bias (PB) determining the output with different primitive behaviors are obtained. The training of the RNN uses back propagation through time (BPTT) method. After that, to generate the learned behaviors, or a more complex behavior which is the combination of the primitive behaviors, a reinforcement learning algorithm: Q-learning (QL) is adopt to determine which PB value is adaptive for the generation. Finally, using a real humanoid robot, the proposed method was confirmed its effectiveness by the results of experiment.

Nocturnal Frontal Lobe Epilepsy: New pathophysiological interpretations

Since the first descriptions of Nocturnal Frontal Lobe Epilepsy (NFLE) many theories have been proposed to explain its pathophysiological mechanisms. The aim of this paper is to formulate a tentative hypothesis designed to unify the clinical, anatomo-physiological, and genetic aspects underlying this condition. According to this hypothesis, NFLE is due to a disorder in the thalamocortical circuit involved in the arousal mechanism. Other cortical-networks involving the limbic system may explain, for instance, primitive behaviors. The role of the cholinergic system and related pathways in the pathogenesis of nocturnal seizures and parasomnias is also discussed.

“Behavior Composed” for Artificial Flying Creature

This study proposes the concepts of “behavior simple” and “behavior composed.” Behavior simple means primitive behavior, and behavior composed is a combination of behaviors simple. An artificial creature first learns some behaviors simple. Then, it learns behavior composed as a combination of behaviors simple, responding to the change of environment. This concept is applied to an Artificial Flying Creature (AFC). The AFC learns two types of flight independently: flapping and gliding. After that, it learns a sophisticated behavior by alternatively selecting these behaviors. Simulation results prove that adequate flight occurs by alternatively using flapping and gliding.

Zooming In on Paid Search Ads—A Consumer-Level Model Calibrated on Aggregated Data

We develop a two-stage consumer-level model of paid search advertising response based on standard aggregated data provided to advertisers by major search engines such as Google or Bing. The proposed model uses behavioral primitives in accord with utility maximization and allows recovering parameters of the heterogeneity distribution in consumer preferences. The model is estimated on a novel paid search data set that includes information on the ad copy. To that end, we develop an original framework to analyze composition and design attributes of paid search ads. Our results allow us to correctly evaluate the effects of specific ad properties on ad performance, taking consumer heterogeneity into account. Another benefit of our approach is allowing recovery of preference correlation across the click-through and conversion stage. Based on the estimated correlation between price- and position-sensitivity, we propose a novel contextual targeting scheme in which a coupon is offered to a consumer depending on the position in which the paid search ad was displayed. Our analysis shows that total revenues from conversion can be increased using this targeting scheme while keeping cost constant.

Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments

A significant obstacle for service robots is the execution of complex tasks in real environments. For example, it is not easy for service robots to find objects that are partially observable and are located at a place which is not identical but near the place where the robots saw them previously. To overcome the challenge effectively, robot knowledge represented as a semantic network can be extremely useful. This paper presents an ontology-based unified robot knowledge framework that integrates low-level data with high-level knowledge for robot intelligence. This framework consists of two sections: knowledge description and knowledge association. Knowledge description includes comprehensively integrated robot knowledge derived from low-level knowledge regarding perceptual features, part objects, metric maps, and primitive behaviors, as well as high-level knowledge about perceptual concepts, objects, semantic maps, tasks, and contexts. Knowledge association uses logical inference with both unidirectional and bidirectional rules. This characteristic enables reasoning to be performed even when only a partial information is available. The experimental results that demonstrate the advantages of using the proposed knowledge framework are also presented.

Navigation towards a goal position: from reactive to generalised learned control

The task of navigating to a target position in space is a fairly common task for a mobile robot. It is desirable that this task is performed even in previously unknown environments. One reactive architecture explored before addresses this challenge by denning a hand-coded coordination of primitive behaviours, encoded by the Potential Fields method. Our first approach to improve the performance of this architecture adds a learning step to autonomously find the best way to coordinate primitive behaviours with respect to an arbitrary performance criterion. Because of the limitations presented by the Potential Fields method, especially in relation to non-convex obstacles, we are investigating the use of Relational Reinforcement Learning as a method to not only learn to act in the current environment, but also to generalise prior knowledge to the current environment in order to achieve the goal more quickly in a non-convex structured environment. We show the results of our previous efforts in reaching goal positions along with our current research on generalised approaches.

A Formalism for Learning from Demonstration*

Abstract The paper describes and formalizes the concepts and assumptions involved in Learning from Demonstration (LFD), a common learning technique used in robotics. LFD-related concepts like goal, generalization, and repetition are here defined, analyzed, and put into context. Robot behaviors are described in terms of trajectories through information spaces and learning is formulated as mappings between some of these spaces. Finally, behavior primitives are introduced as one example of good bias in learning, dividing the learning process into the three stages of behavior segmentation, behavior recognition, and behavior coordination. The formalism is exemplified through a sequence learning task where a robot equipped with a gripper arm is to move objects to specific areas. The introduced concepts are illustrated with special focus on how bias of various kinds can be used to enable learning from a single demonstration, and how ambiguities in demonstrations can be identified and handled.

Automatic emotional expression of a face robot by using a reactive behavior decision model

This paper introduces a face robot named ‘Buddy’ which can perform facial expressions, such as eye-tracking and lip synchronization, via movements of its facial elements (i.e., eyeballs, eyebrows, eyelids, and lips). Buddy has 14 degrees of freedom. To produce the realistic motion of Buddy, we built a ‘Reactive Behavior Decision Model’ which decides not only how to control the rotation angles and speed of facial elements, but to exhibit as well particular emotions that could express the robot’s personality. Buddy’s personality is formed in the model by the accumulated external stimuli and internal status. The process to automatically achieve reactive behavior in the model is classified into three steps: (1) to analyze the external stimuli and identify variations in Buddy’s internal status; (2) to decide the type and degree of emotion based on the robot’s personality; and (3) to generate specific facial expressions and gestures by combining the appropriate primitive behaviors chosen from emotion databases. By using this model, we have proven that Buddy can display various facial expressions and behaviors, at times very reasonable but quite unexpected.