2,406 research outputs found
Towards modeling complex robot training tasks through system identification
Previous research has shown that sensor-motor tasks in mobile robotics applications can be modelled automatically, using NARMAX system identi�cation, where the sensory perception of the robot is mapped to the desired motor commands using non-linear polynomial functions, resulting in a tight coupling between sensing and acting | the robot responds directly to the sensor stimuli without having internal states or memory. However, competences such as for instance sequences of actions, where actions depend on each other, require memory and thus a representation of state. In these cases a simple direct link between sensory perception and the motor commands may not be enough to accomplish the desired tasks. The contribution to knowledge of this paper is to show how fundamental, simple NARMAX
models of behaviour can be used in a bootstrapping process to generate complex behaviours that were so far beyond reach. We argue that as the complexity of the task increases, it is important to estimate the current state of the robot and integrate this information into the system identification process. To achieve this we propose a novel method which relates distinctive locations in
the environment to the state of the robot, using an unsupervised clustering algorithm. Once we estimate the current state of the robot accurately, we combine the state information with the perception of the robot through a bootstrapping method to generate more complex robot tasks: We obtain a polynomial model which models the complex task as a function of predefined low level sensor motor controllers and raw sensory data. The proposed method has been used to teach Scitos G5 mobile robots a number of complex tasks, such as advanced obstacle avoidance, or complex route learning
The Mini-Robot Khepera as a Foraging Animate: Synthesis and Analysis of Behaviour
Löffler A, Klahold J, Rückert U. The Mini-Robot Khepera as a Foraging Animate: Synthesis and Analysis of Behaviour. In: Rückert U, Sitte J, Witkowski U, eds. Proceedings of the 5th International Heinz Nixdorf Symposium: Autonomous Minirobots for Research and Edutainment (AMiRE01). Vol 97. Paderborn, Germany: Heinz Nixdorf Institut, Universität Paderborn; 2001: 93-130.The work presented in this paper deals with the development of a methodology
for resource-efficient behaviour synthesis on autonomous systems. In this context, a definition
of a maximal problem with respect to the resources of a given system is introduced. It
is elucidated by means of an exemplary implementation of the solution to such a problem
using the mini-robot Khepera as the experimental platform. The described task consists of
exploring an unknown and dynamically changing environment, collecting and transporting
objects, which are associated with light-sources, and navigating to a home-base. The critical
point is represented by the accumulated positioning errors in odometrical path-integration
due to slippage. Therefore, adaptive sensor calibration using a specific variant of Kohonen’s
algorithm is applied in two cases to extract symbolic, e.g. geometric, information from the
sub-symbolic sensor data, which is used to enhance position control by landmark mapping
and orientation. In order to successfully handle the arising complex interactions, a heterogeneous
control-architecture based on a parallel implementation of basic behaviours coupled
by a rule-based central unit is proposed
A systematic literature review of decision-making and control systems for autonomous and social robots
In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR
Virtual Reality Games for Motor Rehabilitation
This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion
Autonomous Decision-Making based on Biological Adaptive Processes for Intelligent Social Robots
Mención Internacional en el título de doctorThe unceasing development of autonomous robots in many different scenarios drives a
new revolution to improve our quality of life. Recent advances in human-robot interaction
and machine learning extend robots to social scenarios, where these systems pretend
to assist humans in diverse tasks. Thus, social robots are nowadays becoming real in
many applications like education, healthcare, entertainment, or assistance. Complex
environments demand that social robots present adaptive mechanisms to overcome
different situations and successfully execute their tasks. Thus, considering the previous
ideas, making autonomous and appropriate decisions is essential to exhibit reasonable
behaviour and operate well in dynamic scenarios.
Decision-making systems provide artificial agents with the capacity of making
decisions about how to behave depending on input information from the environment.
In the last decades, human decision-making has served researchers as an inspiration to
endow robots with similar deliberation. Especially in social robotics, where people expect
to interact with machines with human-like capabilities, biologically inspired decisionmaking
systems have demonstrated great potential and interest. Thereby, it is expected
that these systems will continue providing a solid biological background and improve the
naturalness of the human-robot interaction, usability, and the acceptance of social robots
in the following years.
This thesis presents a decision-making system for social robots acting in healthcare,
entertainment, and assistance with autonomous behaviour. The system’s goal is to
provide robots with natural and fluid human-robot interaction during the realisation of
their tasks. The decision-making system integrates into an already existing software
architecture with different modules that manage human-robot interaction, perception,
or expressiveness. Inside this architecture, the decision-making system decides which
behaviour the robot has to execute after evaluating information received from different
modules in the architecture. These modules provide structured data about planned
activities, perceptions, and artificial biological processes that evolve with time that are the
basis for natural behaviour. The natural behaviour of the robot comes from the evolution
of biological variables that emulate biological processes occurring in humans. We also
propose a Motivational model, a module that emulates biological processes in humans for
generating an artificial physiological and psychological state that influences the robot’s
decision-making. These processes emulate the natural biological rhythms of the human organism to produce biologically inspired decisions that improve the naturalness exhibited
by the robot during human-robot interactions. The robot’s decisions also depend on what
the robot perceives from the environment, planned events listed in the robot’s agenda, and
the unique features of the user interacting with the robot.
The robot’s decisions depend on many internal and external factors that influence how
the robot behaves. Users are the most critical stimuli the robot perceives since they are
the cornerstone of interaction. Social robots have to focus on assisting people in their
daily tasks, considering that each person has different features and preferences. Thus,
a robot devised for social interaction has to adapt its decisions to people that aim at
interacting with it. The first step towards adapting to different users is identifying the user
it interacts with. Then, it has to gather as much information as possible and personalise
the interaction. The information about each user has to be actively updated if necessary
since outdated information may lead the user to refuse the robot. Considering these facts,
this work tackles the user adaptation in three different ways.
• The robot incorporates user profiling methods to continuously gather information
from the user using direct and indirect feedback methods.
• The robot has a Preference Learning System that predicts and adjusts the user’s
preferences to the robot’s activities during the interaction.
• An Action-based Learning System grounded on Reinforcement Learning is
introduced as the origin of motivated behaviour.
The functionalities mentioned above define the inputs received by the decisionmaking
system for adapting its behaviour. Our decision-making system has been designed
for being integrated into different robotic platforms due to its flexibility and modularity.
Finally, we carried out several experiments to evaluate the architecture’s functionalities
during real human-robot interaction scenarios. In these experiments, we assessed:
• How to endow social robots with adaptive affective mechanisms to overcome
interaction limitations.
• Active user profiling using face recognition and human-robot interaction.
• A Preference Learning System we designed to predict and adapt the user
preferences towards the robot’s entertainment activities for adapting the interaction.
• A Behaviour-based Reinforcement Learning System that allows the robot to learn
the effects of its actions to behave appropriately in each situation.
• The biologically inspired robot behaviour using emulated biological processes and
how the robot creates social bonds with each user.
• The robot’s expressiveness in affect (emotion and mood) and autonomic functions
such as heart rate or blinking frequency.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Richard J. Duro Fernández.- Secretaria: Concepción Alicia Monje Micharet.- Vocal: Silvia Ross
Incorporating temporal-bounded CBR techniques in real-time agents
Nowadays, MAS paradigm tries to move Computation to a new level of abstraction: Computation as interaction,
where large complex systems are seen in terms of the services they offer, and consequently in
terms of the entities or agents providing or consuming services. However, MAS technology is found to
be lacking in some critical environments as real-time environments. An interaction-based vision of a
real-time system involves the purchase of a responsibility by any entity or agent for the accomplishment
of a required service under possibly hard or soft temporal conditions. This vision notably increases the
complexity of these kinds of systems. The main problem in the architecture development of agents in
real-time environments is with the deliberation process where it is difficult to integrate complex
bounded deliberative processes for decision-making in a simple and efficient way. According to this, this
work presents a temporal-bounded deliberative case-based behaviour as an anytime solution. More specifically,
the work proposes a new temporal-bounded CBR algorithm which facilitates deliberative processes
for agents in real-time environments, which need both real-time and deliberative capabilities.
The paper presents too an application example for the automated management simulation of internal
and external mail in a department plant. This example has allowed to evaluate the proposal investigating
the performance of the system and the temporal-bounded deliberative case-based behaviour.
2010 Elsevier Ltd. All rights reserved.This work is supported by TIN2006-14630-C03-01 projects of the Spanish government, GVPRE/2008/070 project, FEDER funds and CONSOLIDER-INGENIO 2010 under Grant CSD2007-00022.Navarro Llácer, M.; Heras Barberá, SM.; Julian Inglada, VJ.; Botti Navarro, VJ. (2011). Incorporating temporal-bounded CBR techniques in real-time agents. Expert Systems with Applications. 38(3):2783-2796. https://doi.org/10.1016/j.eswa.2010.08.070S2783279638
Learning and Reacting with Inaccurate Prediction: Applications to Autonomous Excavation
Motivated by autonomous excavation, this work investigates solutions to a class of problem where disturbance prediction is critical to overcoming poor performance of a feedback controller, but where the disturbance prediction is intrinsically inaccurate. Poor feedback controller performance is related to a fundamental control problem: there is only a limited amount of disturbance rejection that feedback compensation can provide. It is known, however, that predictive action can improve the disturbance rejection of a control system beyond the limitations of feedback. While prediction is desirable, the problem in excavation is that disturbance predictions are prone to error due to the variability and complexity of soil-tool interaction forces. This work proposes the use of iterative learning control to map the repetitive components of excavation forces into feedforward commands. Although feedforward action shows useful to improve excavation performance, the non-repetitive nature of soil-tool interaction forces is a source of inaccurate predictions. To explicitly address the use of imperfect predictive compensation, a disturbance observer is used to estimate the prediction error. To quantify inaccuracy in prediction, a feedforward model of excavation disturbances is interpreted as a communication channel that transmits corrupted disturbance previews, for which metrics based on the sensitivity function exist. During field trials the proposed method demonstrated the ability to iteratively achieve a desired dig geometry, independent of the initial feasibility of the excavation passes in relation to actuator saturation. Predictive commands adapted to different soil conditions and passes were repeated autonomously until a pre-specified finish quality of the trench was achieved. Evidence of improvement in disturbance rejection is presented as a comparison of sensitivity functions of systems with and without the use of predictive disturbance compensation
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