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

on Wed, 12/16/2015 - 14:56

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.

 

 

 

 

 

Robot learning through dynamical systems (PhD thesis)

on Wed, 12/09/2015 - 14:12

During my PhD, I've worked mainly on Reservoir Computing (RC) architectures with application to modeling cognitive capabilities for mobile robots from sensor data and sometimes through interaction with the environment.

Reservoir Computing (RC) is an efficient method for trainning recurrent neural networks, which can handle spatio-temporal processing tasks, such as speech recognition. These networks are also biological plausible, as recently argued in the literature.

In my case, I used these RC networks for modeling a wide range of capabilities for mobile robots, such as:

These tasks were modeled basically using regression for learning behaviors or classification for discrete localization.

My PhD thesis can be download here. It is entitled: "Reservoir Computing Architectures for Modeling Robot Navigation Systems".

My publications are listed and can be downloaded in Google Scholar or here.

Some simulated and real robots employed in the experiments:


 

Environment used for localization experiments using the real e-puck robot:


 

After using unsupervised learning methods for self-localization, the plots below show the mean activation of place cells as a function of the robot position in the environment.
Red denotes a high response whereas blue denotes a low response.
 

It is possible to perform map generation through sensory prediction given the robot position as input. Black points represent the sensory readings whereas gray points are the robot trajectory.

 

Online recurrent neural network learning for control of nonlinear plants in oil and gas production platforms

on Wed, 10/17/2018 - 12:54

This research line aims at designing adaptive controllers by using Echo State Networks (ESN) as a efficient data-driven method for training recurrent neural networks capable of controlling complex nonlinear plants, with a focus on oil and gas production platforms from Petrobras.

The resulting ESN-based controllers should learn inverse models of the controlled plant in an online fashion by interacting with the industrial plant and observing its dynamical behaviors.

In collaboration with supervised Master Student Jean P. Jordanou.

Well model. Figure by Jahanshahi et al. (2012).          

 

Manifold connecting two oil wells and a riser. Figure by Jordanou.

Scheme of Adaptive ESN-based controller and nonlinear plant. Figure by Jordanou

POLSAB aims at advancing the state-of-the-art in safe imitation learning for high-dimensional domains in an end- to-end approach. We focus on two main applications: autonomous robot navigation and self-driving car simulations. In order to design efficient and safe policies (which map observations to actions) for these tasks, it is necessary more than just using behavioral cloning which basically applies supervised learning on a labelled dataset.

Control tasks usually have the issue of cascading errors. This happens when the controller's policy does not take into account the feedback loops of controller mistakes: little deviations from the desired reference track (the street lane, or the robot path) causes the error to feedback into the policy as new observations arise, until no valid action is possible anymore.

In order to fix these problems, in this project, we will use a recently introduced framework called Generative Adversarial Imitation Learning (GAIL) for learning robust policies by imitation learning for the robot and the simulated vehicle. To minimize the risk of high-cost events (accidents), the risk-averse version of GAIL will be extended to our application domains.

A second approach will tackle the recent framework of option-critic in Reinforcement Learning (RL), where by defining a reward function (a qualitative measure of the agent's behavior) it is possible to learn robust control policies by trial and error. This method also takes into account temporal abstractions in the policy mappings by creating hierarchies of behaviors in time, which makes it possible to scale up reinforcement learning.

Finally, this project will contribute to research in safe AI by investigating risk-sensitive methods in applications where this is of paramount importance in order to position Luxembourg as a important player in billion dollars industries: safe, robust AI agents in real-world settings (e.g. trading, autonomous driverless vehicles, service robotics).

SUPPORT (if project is realized with FNR support):

  • FNR Luxembourg
  • SnT/University of Luxembourg
  • Institute for Robotics and Process Control at the Technische Universität Braunschweig (Prof. Dr. Jochen Steil)
  • Google DeepMind (Dr. Raia Hadsell)

PLATFORMS TO BE USED IN THE PROJECT:

                 

Turtlebot Waffle with camera and LiDAR sensors for autonomous navigation

                

   

CARLA: simulation for self-driving/autonomous cars in urban environments

 

TORCS: simulation for racing/road autonomous cars

 

Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing

on Wed, 01/20/2016 - 21:02

Autonomous mobile robots form an important research topic in the field of robotics due to their near-term applicability in the real world as domestic service robots. These robots must be designed in an efficient way using training sequences. They need to be aware of their position in the environment and also need to create models of it for deliberative planning. These tasks have to be performed using a limited number of sensors with low accuracy, as well as with a restricted amount of computational power. In this contribution we show that the recently emerged paradigm of Reservoir Computing (RC) is very well suited to solve all of the above mentioned problems, namely learning by example, robot localization, map and path generation. Reservoir Computing is a technique which enables a system to learn any time-invariant filter of the input by training a simple linear regressor that acts on the states of a highdimensional but random dynamic system excited by the inputs. In addition, RC is a simple technique featuring ease of training, and low computational and memory demands.

Keywords: reservoir computing, generative modeling, map learning, T-maze task, road sign problem, path generation

 

 

 

 

 

 

Related Publications

  1. Eric AntoneloBenjamin Schrauwen and Jan Van Campenhout Generative Modeling of Autonomous Robots and their Environments using Reservoir Computing Neural Processing Letters, Vol. 26(3), pp. 233-249 (2007)   

 

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