Alejandra Barrera

Biologically-inspired robot spatial cognition based on rat neurophysiological studies

Abstract This paper presents a robot architecture with spatial cognition and navigation capabilities that captures some properties of the rat brain structures involved in learning and memory. This architecture relies on the integration of kinesthetic and visual information derived from artificial landmarks, as well as on Hebbian learning, to build a holistic topological-metric spatial representation during exploration, and employs reinforcement learning by means of an Actor-Critic architecture to enable learning and unlearning of goal locations. From a robotics perspective, this work can be placed in the gap between mapping and map exploitation currently existent in the SLAM literature. The exploitation of the cognitive map allows the robot to recognize places already visited and to find a target from any given departure location, thus enabling goal-directed navigation. From a biological perspective, this study aims at initiating a contribution to experimental neuroscience by providing the system as a tool to test with robots hypotheses concerned with the underlying mechanisms of rats’ spatial cognition. Results from different experiments with a mobile AIBO robot inspired on classical spatial tasks with rats are described, and a comparative analysis is provided in reference to the reversal task devised by O’Keefe in 1983.

Comparative Experimental Studies on Spatial Memory and Learning in Rats and Robots

The study of behavioral and neurophysiological mechanisms involved in rat spatial cognition provides a basis for the development of computational models and robotic experimentation of goal-oriented learning tasks. These models and robotics architectures offer neurobiologists and neuroethologists alternative platforms to study, analyze and predict spatial cognition based behaviors. In this paper we present a comparative analysis of spatial cognition in rats and robots by contrasting similar goal-oriented tasks in a cyclical maze, where studies in rat spatial cognition are used to develop computational system-level models of hippocampus and striatum integrating kinesthetic and visual information to produce a cognitive map of the environment and drive robot experimentation. During training, Hebbian learning and reinforcement learning, in the form of Actor-Critic architecture, enable robots to learn the optimal route leading to a goal from a designated fixed location in the maze. During testing, robots exploit maximum expectations of reward stored within the previously acquired cognitive map to reach the goal from different starting positions. A detailed discussion of comparative experiments in rats and robots is presented contrasting learning latency while characterizing behavioral procedures during navigation such as errors associated with the selection of a non-optimal route, body rotations, normalized length of the traveled path, and hesitations. Additionally, we present results from evaluating neural activity in rats through detection of the immediate early gene Arc to verify the engagement of hippocampus and striatum in information processing while solving the cyclical maze task, such as robots use our corresponding models of those neural structures.

Rat-inspired model of robot target learning and place recognition

We present a model designed on the basis of the rats brain neurophysiology to provide a robot with spatial cognition and goal-oriented navigation capabilities. We describe target learning and place recognition processes in rats as basis for topological map building and exploitation by robots. We experiment with the model in different maze configurations by training a robot to find the goal starting from a fixed location, and by testing it to reach the same target from new different starting locations.

Bio-inspired Model of Robot Spatial Cognition: Topological Place Recognition and Target Learning

In this paper we present a model designed on the basis of the rats brain neurophysiology to provide a robot with spatial cognition and goal-oriented navigation capabilities. We describe place representation and recognition processes in rats as the basis for topological map building and exploitation by robots. We experiment with the model by training a robot to find the goal in a maze starting from a fixed location, and by testing it to reach the same target from new different starting locations.

Computational modeling of spatial cognition in rats and robotic experimentation: Goal-oriented navigation and place recognition in multiple directions

A computational model of spatial cognition in rats is used to control an autonomous mobile robot while solving a spatial task within a cyclic maze. In this paper we evaluate the robotpsilas behavior in terms of place recognition in multiple directions and goal-oriented navigation against the results derived from experimenting with laboratory rats solving the same spatial task in a similar maze. We provide a general description of the bio-inspired model, and a comparative behavioral analysis between rats and robot.

Spatial cognition: robot target localization in open arenas based on rat studies

We describe our latest work in understanding spatial localization in open arenas based on rat studies and corresponding modeling with simulated and physical robots. The studies and experiments focus on goal-oriented navigation where both rats and robots exploit distal cues to localize and find a goal in an open environment. The task involves training of both rats and robots to find the shortest path to the goal from multiple starting points in the environment. The spatial cognition model is based on the rat’s brain neurophysiology of the hippocampus extending previous work by analyzing granularity of localization in relation to a varying number and position of landmarks. The robot integrates internal and external information to create a topological map of the environment and to generate shortest routes to the goal through path integration. One of the critical challenges for the robot is to analyze the similarity of positions and distinguish among different locations using visual cues and previous paths followed to reach the current position. We describe the robotics architecture used to develop, simulate and experiment with physical robots.

Learning Spatial Localization: From Rat Studies to Computational Models of the Hippocampus

In his landmark article, Richard Morris (1981) introduced a set of rat experiments intended “to demonstrate that rats can rapidly learn to locate an object that they can never see, hear, or smell provided it remains in a fixed spatial location relative to distal room cues” (p. 239). These experimental studies have greatly impacted our understanding of rat spatial cognition. In this article, we address a spatial cognition model primarily based on hippocampus place cell computation where we extend the prior Barrera–Weitzenfeld model (2008) intended to allow navigation in mazes containing corridors. The current work extends beyond the limitations of corridors to enable navigation in open arenas where a rat may move in any direction at any time. The extended work reproduces Morriss rat experiments through virtual rats that search for a hidden platform using visual cues in a circular open maze analogous to the Morris water maze experiments. We show results with virtual rats comparing them to Morriss original studies with rats.

Book review: David Kortenkamp, R. Peter Bonasso and Robin Murphy, Artificial Intelligence and Mobile Robots, AAAI Press/The MIT Press, USA, ISBN: 0-262-61137-6, 1998 (390pp.).

About the reviewer M. Sami Fadali earned a BS in Electrical Engineering from Cairo University in 1974, an MS from the Control Systems Center, UMIST, England, in 1977 and a Ph.D. from the University of Wyoming in 1980. He was an Assistant Professor of Electrical Engineering at the University of King Abdul Aziz, Jeddah, Saudi Arabia, 1981–1983. From 1983–1985, he was a Post Doctoral Fellow at Colorado State University. In 1985, he joined the Electrical Engineering Department at the University of Nevada, Reno, where he is currently Professor of Electrical Engineering. In 1994, he was a visiting professor at Oakland University and GM Research and Development Labs. He spent the summer of 2000 as a Senior Engineer at TRW, San Bernardino. His research interests are in the areas of robust control, robust stability, fault detection and fuzzy logic control. M. Sami Fadali is a senior member of the IEEE.

Solving uncertainty during robot navigation by integrating grid cell and place cell firing based on rat spatial cognition studies

The efficient resolution of spatial localization is a key challenge in autonomous mobile robots. We describe in this paper our latest work in understanding spatial localization based on rat behavioral and neural studies. We develop a grid cell neural model based on studies in the Medial Entorhinal Cortex that integrates to a place cell neural model in the Hippocampus to generate “neural odometry” and spatial localization in the rat. We evaluate the model through simulated and physical robot experiments using a Khepera III autonomous robot in a laboratory environment.

Allothetic and idiothetic sensor fusion in rat-inspired robot localization

We describe a spatial cognition model based on the rats brain neurophysiology as a basis for new robotic navigation architectures. The model integrates allothetic (external visual landmarks) and idiothetic (internal kinesthetic information) cues to train either rat or robot to learn a path enabling it to reach a goal from multiple starting positions. It stands in contrast to most robotic architectures based on SLAM, where a map of the environment is built to provide probabilistic localization information computed from robot odometry and landmark perception. Allothetic cues suffer in general from perceptual ambiguity when trying to distinguish between places with equivalent visual patterns, while idiothetic cues suffer from imprecise motions and limited memory recalls. We experiment with both types of cues in different maze configurations by training rats and robots to find the goal starting from a fixed location, and then testing them to reach the same target from new starting locations. We show that the robot, after having pre-explored a maze, can find a goal with improved efficiency, and is able to (1) learn the correct route to reach the goal, (2) recognize places already visited, and (3) exploit allothetic and idiothetic cues to improve on its performance. We finally contrast our biologically-inspired approach to more traditional robotic approaches and discuss current work in progress.