Spatial Representation and Navigation in a Bio-inspired Robot

Abstract

Abstract.A biologically inspired computational model of rodent representation-based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlapping receptive fields. Goal navigation is performed using reinforcement learning in continuous state and action spaces, where the state space is represented by population activity of the place cells. The model is able to reproduce a number of behavioral and neurophysiological data on rodents. Performance of the model was tested on both simulated and real mobile Khepera robots in a set of behavioral tasks and is comparable to the performance of animals in similar tasks.

Locating and navigation mechanism based on place-cell and grid-cell models

“…In other words, they may reveal the answers to the following two questions: where we are now and where we will go? Previous model studies (Arleo et al 2004;Sheynikhovich et al 2005;Krichmar et al 2005;Arleo and Gerstner 2000;Kulvicius et al 2008) focus on the possible navigation mechanism based on place cells or grid cells. None of the models goes with these two kinds of cells, or with their interaction in navigation tasks, which motivated our present study.…”

“…These particular locations within an environment are named place fields (PFs). Many models have been founded for place cells, using such tools as Gaussian function in (O'Keefe and Burgess 1996;Hartley et al 2000;Foster et al 2000), neuronal plasticity in (Arleo et al 2004;Sheynikhovich et al 2005;Krichmar et al 2005), independent component analysis in (Franzius et al 2007), and circuit-level model in (Jayet Bray et al 2010). These studies most focus on fire description and place field (PF) formation.…”

“…Consequently, some models were proposed based on place cells in (Arleo et al 2004;Sheynikhovich et al 2005;Krichmar et al 2005;Arleo and Gerstner 2000;Kulvicius et al 2008). It is commonly believed that place cells may be driven by grid cells (McNaughton et al 2006;Solstad et al 2006a, b;Molter and Yamaguchi 2008;Si and Treves 2009 Sreenivasan and Fiete 2011).…”

Adaptive Landmark-Based Navigation System Using Learning Techniques

“…Some of them use internal representations (e.g. generalized voronoi diagrams [9], place cells [10], etc). Others use potential fields [11].…”

Reinforcement Learning for Scheduling of Maintenance

“…Due to the use of a simple, final reward, reinforcement learning has found applications in interaction scenarios where an agent receives feedback from a user at the end of a sequence of actions such as dialogue management [11], visual homing and navigation [12,13,14,15,16], human-computer/robot interaction [17], robot navigation [18,19] and for learning skills in the Robocup Soccer Competition [20,21,22]. There have already been some initial attempts to explore reinforcement learning for restricted tasks in scheduling, routing, and network optimisation.…”