Using Echo State Networks for Robot Navigation Behavior Acquisition

International audienceRobot Behavior Learning by Demonstration deals with the ability for a robot to learn a behavior from one or several demonstrations provided by a human teacher, possibly through tele-operation or imitation. This implies controllers that can address both (1) the feature selection problem related to a great amount of mostly irrelevant sensory data and (2) dealing with temporal sequences of demonstrations. Echo State Networks have been proposed recently for time series prediction and have been shown to perform remarkably well on this kind of data. In this paper, we introduce ESN to robot behavior acquisition in the scope of a mobile robot performing navigation tasks. ESN actually show comparable and even better performance with that of other algorithms from the literature in similar experimental conditions. Moreover, some properties regarding dynamics of ESN in the context of learning by demonstration are investigated

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Memory-Enhanced Evolutionary Robotics: The Echo State Network Approach

International audienceInterested in Evolutionary Robotics, this paper focuses on the acquisition and exploitation of memory skills. The targeted task is a well-studied benchmark problem, the Tolman maze, requiring in principle the robotic controller to feature some (limited) counting abilities. An elaborate experimental setting is used to enforce the controller generality and prevent opportunistic evolution from mimicking deliberative skills through smart reactive heuristics. The paper compares the prominent NEAT approach, achieving the non-parametric optimization of Neural Nets, with the evolutionary optimization of Echo State Networks, pertaining to the recent field of Reservoir Computing. While both search spaces offer a sufficient expressivity and enable the modelling of complex dynamic systems, the latter one is amenable to robust parametric, linear optimization with Covariance Matrix Adaptation-Evolution Strategies

On Learning Navigation Behaviors for Small Mobile Robots With Reservoir Computing Architectures

Abstract—This work proposes a general Reservoir Computing (RC) learning framework which can be used to learn navigation behaviors for mobile robots in simple and complex unknown, partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior which can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using an hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors towards the goal. Index Terms—robot navigation, reservoir computing, rein-forcement learning, goal-directed navigation, recurrent neural networks, echo state network, sensory-motor coupling. I

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

Imitation Learning of an Intelligent Navigation System for Mobile Robots using Reservoir Computing

The design of an autonomous navigation system for mobile robots can be a tough task. Noisy sensors, unstructured environments and unpredictability are among the problems which must be overcome. Reservoir computing (RC) uses a randomly created recurrent neural network (the reservoir) which functions as a temporal kernel of rich dynamics that projects the input to a high dimensional space. This projection is mapped into the desired output (only this mapping must be learned with standard linear regression methods).In this work, RC is used for imitation learning of navigation behaviors generated by an intelligent navigation system in the literature. Obstacle avoidance, exploration and target seeking behaviors are reproduced with an increase in stability and robustness over the original controller. Experiments also show that the system generalizes the behaviors for new environments

Reservoir Computing Architectures for Modeling Robot Navigation Systems

This thesis proposes a new efficient and biologically inspired way of modeling navigation tasks for autonomous mobile robots having restrictions on cost, energy consumption, and computational complexity (such as household and assistant robots). It is based on the recently proposed Reservoir Computing approach for training Recurrent Neural Networks. Robot Navigation Systems Autonomous mobile robots must be able to safely and purposefully navigate in complex dynamic environments, preferentially considering a restricted amount of computational power as well as limited energy consumption. In order to turn these robots into commercially viable domestic products with intelligent, abstract computational capabilities, it is also necessary to use inexpensive sensory apparatus such as a few infra-red distance sensors of limited accuracy. Current state-of-the-art methods for robot localization and navigation require fully equipped robotic platforms usually possessing expensive laser scanners for environment mapping, a considerable amount of computational power, and extensive explicit modeling of the environment and of the task. This thesis The research presented in this thesis is a step towards creating intelligent autonomous mobile robots with abstract reasoning capabilities using a limited number of very simple raw noisy sensory signals, such as distance sensors. The basic assumption is that the low-dimensional sensory signal can be projected into a high-dimensional dynamic space where learning and computation is performed by linear methods (such as linear regression), overcoming sensor aliasing problems commonly found in robot navigation tasks. This form of computation is known in the literature as Reservoir Computing (RC), and the Echo State Network is a particular RC model used in this work and characterized by having the high-dimensional space implemented by a discrete analog recurrent neural network with fading memory properties. This thesis proposes a number of Reservoir Computing architectures which can be used in a variety of autonomous navigation tasks, by modeling implicit abstract representations of an environment as well as navigation behaviors which can be sequentially executed in the physical environment or simulated as a plan in deliberative goal-directed tasks. Navigation attractors A navigation attractor is a reactive robot behavior defined by a temporal pattern of sensory-motor coupling through the environment space. Under this scheme, a robot tends to follow a trajectory with attractor-like characteristics in space. These navigation attractors are characterized by being robust to noise and unpredictable events and by having inherent collision avoidance skills. In this work, it is shown that an RC network can model not only one behavior, but multiple navigation behaviors by shifting the operating point of the dynamical reservoir system into different \emph{sub-space attractors} using additional external inputs representing the selected behavior. The sub-space attractors emerge from the coupling existing between the RC network, which controls the autonomous robot, and the environment. All this is achieved under an imitation learning framework which trains the RC network using examples of navigation behaviors generated by a supervisor controller or a human. Implicit spatial representations From the stream of sensory input given by distance sensors, it is possible to construct implicit spatial representations of an environment by using Reservoir Computing networks. These networks are trained in a supervised way to predict locations at different levels of abstraction, from continuous-valued robot's pose in the global coordinate's frame, to more abstract locations such as small delimited areas and rooms of a robot environment. The high-dimensional reservoir projects the sensory input into a dynamic system space, whose characteristic fading memory disambiguates the sensory space, solving the sensor aliasing problems where multiple different locations generate similar sensory readings from the robot's perspective. Hierarchical networks for goal-directed navigation It is possible to model navigation attractors and implicit spatial representations with the same type of RC network. By constructing an hierarchical RC architecture which combines the aforementioned modeling skills in two different reservoir modules operating at different timescales, it is possible to achieve complex context-dependent sensory-motor coupling in unknown environments. The general idea is that the network trained to predict the location and orientation of the robot in this architecture can be used to select appropriate navigation attractors according to the current context, by shifting the operating point of the navigation reservoir to a sub-space attractor. As the robot navigates from one room to the next, a corresponding context switch selects a new reactive navigation behavior. This continuous sequence of context switches and reactive behaviors, when combined with an external input indicating the destination room, leads ultimately to a goal-directed navigation system, purely trained in a supervised way with examples of sensory-motor coupling. Generative modeling of environment-robot dynamics RC networks trained to predict the position of the robot from the sensory signals learns forward models of the robot. By using a generative RC network which predicts not only locations but also sensory nodes, it is possible to use the network in the opposite direction for predicting local environmental sensory perceptions from the robot position as input, thus learning an inverse model. The implicit map learned by forward models can be made explicit, by running the RC network in reverse: predict the local sensory signals given the location of the robot as input (inverse model). which are fed back to the reservoir, it is possible to internally predict future scenarios and behaviors without actually experiencing them in the current environment (a process analogous to dreaming), constituting a planning-like capability which opens new possibilities for deliberative navigation systems. Unsupervised learning of spatial representations In order to achieve a higher degree of autonomy in the learning process of RC-based navigation systems which use implicit learned models of the environment for goal-directed navigation, a new architecture is proposed. Instead of using linear regression, an unsupervised learning method which extracts slowly-varying output signals from the reservoir states, called Slow Feature Analysis, is used to generate self-organized spatial representations at the output layer, without the requirement of labeling training data with the desired locations. It is shown experimentally that the proposed RC-SFA architecture is empowered with an unique combination of short-term memory and non-linear transformations which overcomes the hidden state problem present in robot navigation tasks. In addition, experiments with simulated and real robots indicate that spatial activations generated by the trained network show similarities to the activations of CA1 hippocampal cells of rats (a specific group of neurons in the hippocampus)