Learning obstacle avoidance with an operant behavioral model

Artificial intelligence researchers have been attracted by the idea of having robots learn how to accomplish a task, rather than being told explicitly. Reinforcement learning has been proposed as an appealing framework to be used in controlling mobile agents. Robot learning research, as well as research in biological systems, face many similar problems in order to display high flexibility in performing a variety of tasks. In this work, the controlling of a vehicle in an avoidance task by a previously developed operant learning model (a form of animal learning) is studied. An environment in which a mobile robot with proximity sensors has to minimize the punishment for colliding against obstacles is simulated. The results were compared with the Q-Learning algorithm, and the proposed model had better performance. In this way a new artificial intelligence agent inspired by neurobiology, psychology, and ethology research is proposed.Fil: Gutnisky, D. A.. Universidad de Buenos Aires. Facultad de Ingeniería.Instituto de Ingeniería Biomédica; ArgentinaFil: Zanutto, Bonifacio Silvano. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería.Instituto de Ingeniería Biomédica; Argentin

Application of Biological Learning Theories to Mobile Robot Avoidance and Approach Behaviors

We present a neural network that learns to control approach and avoidance behaviors in a mobile robot using the mechanisms of classical and operant conditioning. Learning, which requires no supervision, takes place as the robot moves around an environment cluttered with obstacles and light sources. The neural network requires no knowledge of the geometry of the robot or of the quality, number or configuration of the robot's sensors. In this article we provide a detailed presentation of the model, and show our results with the Khepera and Pioneer 1 mobile robots.Office of Naval Research (N00014-96-1-0772, N00014-95-1-0409

A Model of Operant Conditioning for Adaptive Obstacle Avoidance

We have recently introduced a self-organizing adaptive neural controller that learns to control movements of a wheeled mobile robot toward stationary or moving targets, even when the robot's kinematics arc unknown, or when they change unexpectedly during operation. The model has been shown to outperform other traditional controllers, especially in noisy environments. This article describes a neural network module for obstacle avoidance that complements our previous work. The obstacle avoidance module is based on a model of classical and operant conditioning first proposed by Grossberg ( 1971). This module learns the patterns of ultrasonic sensor activation that predict collisions as the robot navigates in an unknown cluttered environment. Along with our original low-level controller, this work illustrates the potential of applying biologically inspired neural networks to the areas of adaptive robotics and control.Office of Naval Research (N00014-95-1-0409, Young Investigator Award

Obstacle Avoidance by Means of an Operant Conditioning Model

This paper describes the application of a model of operant conditioning to the problem of obstacle avoidance with a wheeled mobile robot. The main characteristic of the applied model is that the robot learns to avoid obstacles through a learning-by-doing cycle without external supervision. A series of ultrasonic sensors act as Conditioned Stimuli (CS), while collisions act as an Unconditioned Stimulus (UCS). By experiencing a series of movements in a cluttered environment, the robot learns to avoid sensor activation patterns that predict collisions, thereby learning to avoid obstacles. Learning generalizes to arbitrary cluttered environments. In this work we describe our initial implementation using a computer simulation

A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring

This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systems’ resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehicles’ adaptive navigation system and long-term mission capabilities are shown in the experimental results.This work was partially supported by the BUSCAMOS Project (ref. 1003211003700) under the program DN8644 COINCIDENTE of the Spanish Defense Ministry, the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia-19895/GERM/15)”, and the Spanish Government’s cDrone (ref. TIN2013-45920-R) and ViSelTR (ref. TIN2012-39279) projects

Intelligent Navigation for a Solar Powered Unmanned Underwater Vehicle

In this paper, an intelligent navigation system for an unmanned underwater vehicle powered by renewable energy and designed for shadow water inspection in missions of a long duration is proposed. The system is composed of an underwater vehicle, which tows a surface vehicle. The surface vehicle is a small boat with photovoltaic panels, a methanol fuel cell and communication equipment, which provides energy and communication to the underwater vehicle. The underwater vehicle has sensors to monitor the underwater environment such as sidescan sonar and a video camera in a flexible configuration and sensors to measure the physical and chemical parameters of water quality on predefined paths for long distances. The underwater vehicle implements a biologically inspired neural architecture for autonomous intelligent navigation. Navigation is carried out by integrating a kinematic adaptive neuro‐controller for trajectory tracking and an obstacle avoidance adaptive neuro‐  controller. The autonomous underwater vehicle is capable of operating during long periods of observation and monitoring. This autonomous vehicle is a good tool for observing large areas of sea, since it operates for long periods of time due to the contribution of renewable energy. It correlates all sensor data for time and geodetic position. This vehicle has been used for monitoring the Mar Menor lagoon.Supported by the Coastal Monitoring System for the Mar Menor (CMS‐  463.01.08_CLUSTER) project founded by the Regional Government of Murcia, by the SICUVA project (Control and Navigation System for AUV Oceanographic Monitoring Missions. REF: 15357/PI/10) founded by the Seneca Foundation of Regional Government of Murcia and by the DIVISAMOS project (Design of an Autonomous Underwater Vehicle for Inspections and oceanographic mission‐UPCT: DPI‐ 2009‐14744‐C03‐02) founded by the Spanish Ministry of Science and Innovation from Spain

A contextual behavioral approach to the study of (persecutory) delusions

Throughout the past century the topic of delusions has mainly been studied by researchers operating at the mental level of analysis. According to this perspective, delusional beliefs, as well as their emergence and persistence, stem from an interplay between (dysfunctional) mental representations and processes. Our paper aims to provide a starting point for researchers and clinicians interested in examining the topic of delusions from a functional-analytic perspective. We begin with a brief review of the research literature with a particular focus on persecutory delusions. Thereafter we introduce Contextual Behavioral Science (CBS), Relational Frame Theory (RFT) and a behavioral phenomenon known as arbitrarily applicable relational responding (AARR). Drawing upon AARR, and recent empirical developments within CBS, we argue that (persecutory) delusions may be conceptualized, studied and influenced using a functional-analytic approach. We consider future directions for research in this area as well as clinical interventions aimed at influencing delusions and their expression

Neural Models of Normal and Abnormal Behavior: What Do Schizophrenia, Parkinsonism, Attention Deficit Disorder, and Depression Have in Common?

Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

Emotional and motivational pain processing : current state of knowledge and perspectives in translational research

Pain elicits fear and anxiety and promotes escape, avoidance, and adaptive behaviors that are essential for survival. When pain persists, motivational priority and attention shift to pain-related information. Such a shift often results in impaired functionality, leading to maladaptive pain-related fear and anxiety and escape and avoidance behaviors. Neuroimaging studies in chronic pain patients have established that brain activity, especially in cortical and mesolimbic regions, is different from activity observed during acute pain in control subjects. In this review, we discuss the psychophysiological and neuronal factors that may be associated with the transition to chronic pain. We review information from human studies on neural circuits involved in emotional and motivational pain processing and how these circuits are altered in chronic pain conditions. We then highlight findings from animal research that can increase our understanding of the molecular and cellular mechanisms underlying emotional-motivational pain processing in the brain. Finally, we discuss how translational approaches incorporating results from both human and animal investigations may aid in accelerating the discovery of therapies

Emergence of Diversity in a Group of Identical Bio-Robots

Learning capabilities, often guided by competition/cooperation, play a fundamental and ubiquitous role in living beings. Moreover, several behaviours, such as feeding and courtship, involve environmental exploration and exploitation, including local competition, and lead to a global benefit for the colony. This can be considered as a form of global cooperation, even if the individual agent is not aware of the overall effect. This paper aims to demonstrate that identical biorobots, endowed with simple neural controllers, can evolve diversified behaviours and roles when competing for the same resources in the same arena. These behaviours also produce a benefit in terms of time and energy spent by the whole group. The robots are tasked with a classical foraging task structured through the cyclic activation of resources. The result is that each individual robot, while competing to reach the maximum number of available targets, tends to prefer a specific sequence of subtasks. This indirectly leads to the global result of task partitioning, whereby the cumulative energy spent, in terms of the overall travelled distance and the time needed to complete the task, tends to be minimized. A series of simulation experiments is conducted using different numbers of robots and scenarios: the common emergent result obtained is the role specialization of each robot. The description of the neural controller and the specialization mechanisms are reported in detail and discussed