This document summarizes a paper that proposes a general framework for machine learning of motor skills in robots. It discusses three key components: (1) representation of motor skills using dynamic motor primitives, (2) learning algorithms like natural actor-critic and reward-weighted regression to learn and improve the motor primitives, and (3) execution of skills on robot systems by mapping primitives to motor commands. The framework separates learning of motor tasks from real-time control, allowing long-term learning from demonstrations and reinforcement as well as fast policy improvement. It is evaluated in simulations of robot arm control tasks and a physical hitting task.
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKS.ijaia
This document summarizes a research paper that proposes using an artificial neural network to analyze the best route for automobile vehicles in traffic conditions. It presents a neural network architecture with 5 input parameters (distance, traffic volume, number of signals, road condition, travel time) and 5 output routes. The network is trained on 243 input combinations and tested on unseen data. Test results showed the network with backpropagation had 91.2% accuracy and lower error than without backpropagation. The research aims to help commuters optimize routes based on traffic parameters, though assumptions about static conditions limit realism. Future work will analyze more dynamic traffic flow conditions.
The document summarizes a proposed fuzzy logic-based joint space path planning system for a 3 degree-of-freedom robot manipulator. The system is composed of three separate fuzzy logic units that each control one of the manipulator joints. The inputs and outputs of each fuzzy block control the change in joint position for each time step. Simulation results show the robot is able to reach the goal configuration successfully using this approach. The fuzzy logic method is able to meet real-time requirements for robot motion planning without requiring an exact model of the robot.
This document discusses algorithms for avoiding kinematic singularities in 6-DOF robotic manipulators controlled in real time using a teaching pendant. It proposes two algorithms: (1) non-redundancy avoidance using damped least squares to modify the inverse kinematic solution near singularities, and (2) redundancy avoidance using a potential function based on manipulability to incorporate singularity avoidance for redundant manipulators. The algorithms are experimentally tested on a DENSO VP-6242G robot to evaluate performance near shoulder and wrist singularities during teaching pendant controlled motion.
Kinematic control with singularity avoidance for teaching-playback robot mani...Baron Y.S. Yong
This article proposes and investigates three methods for avoiding kinematic singularities in a teaching-playback robot manipulator system:
1. Nonredundancy singularity avoidance (NRSA) and redundancy singularity avoidance (RSA) modify the Jacobian matrix to reduce both position and orientation errors or prioritize position error reduction.
2. Point-to-point singularity avoidance (PTPSA) moves the end-effector through singular regions via joint-interpolated control without maintaining position and orientation.
Experimental case studies evaluate the three methods when the end-effector approaches wrist and shoulder singularities. Results show the methods effectively avoid singularities and enhance robot capability for industrial automation.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Auto mobile vehicle direction in road traffic using artificial neural networkscsandit
This document presents a study on using an artificial neural network to analyze automobile vehicle routing in traffic conditions. Specifically, it aims to select the optimal route given inputs like distance, traffic volume, number of signals, road conditions and travel time. A backpropagation neural network with 5 input nodes, 10 hidden nodes and 5 output nodes representing 5 potential routes is created. The network is trained on 180 combinations of the 5 input parameters to learn the best route. Testing shows the backpropagation network achieves 91.2% accuracy in route selection, outperforming a network without backpropagation. The study concludes the network provides an effective approach for dynamic vehicle routing analysis, though some assumptions could be improved.
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKScsandit
So far Most of the current work on this area deals with traffic volume prediction during peak hours and the reasons behind accidents only. This work presents the analysis of automobile vehicle directing in various traffic flow conditions using Artificial neural network architecture.
Now a days, due to unprecedented increase in automobile vehicular traffic especially in metro-Politian cities, it has become highly imperative that we must choose an optimum road route in accordance with our requirements. The requirements are : volume of the traffic, Distance
between source and destination, no of signals in between the source and destination, the nature of the road condition , fuel consumption and Travel Timing. Artificial Neural networks, a soft computing technique, modeled after brain biological neuron functioning, helps to obtain the
required road way or route as per the training given to it. Here we make use of Back propagation network, which changes the weights value of the hidden layers, thereby activation function value which fires the neuron to get the required output.
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKS.ijaia
This document summarizes a research paper that proposes using an artificial neural network to analyze the best route for automobile vehicles in traffic conditions. It presents a neural network architecture with 5 input parameters (distance, traffic volume, number of signals, road condition, travel time) and 5 output routes. The network is trained on 243 input combinations and tested on unseen data. Test results showed the network with backpropagation had 91.2% accuracy and lower error than without backpropagation. The research aims to help commuters optimize routes based on traffic parameters, though assumptions about static conditions limit realism. Future work will analyze more dynamic traffic flow conditions.
The document summarizes a proposed fuzzy logic-based joint space path planning system for a 3 degree-of-freedom robot manipulator. The system is composed of three separate fuzzy logic units that each control one of the manipulator joints. The inputs and outputs of each fuzzy block control the change in joint position for each time step. Simulation results show the robot is able to reach the goal configuration successfully using this approach. The fuzzy logic method is able to meet real-time requirements for robot motion planning without requiring an exact model of the robot.
This document discusses algorithms for avoiding kinematic singularities in 6-DOF robotic manipulators controlled in real time using a teaching pendant. It proposes two algorithms: (1) non-redundancy avoidance using damped least squares to modify the inverse kinematic solution near singularities, and (2) redundancy avoidance using a potential function based on manipulability to incorporate singularity avoidance for redundant manipulators. The algorithms are experimentally tested on a DENSO VP-6242G robot to evaluate performance near shoulder and wrist singularities during teaching pendant controlled motion.
Kinematic control with singularity avoidance for teaching-playback robot mani...Baron Y.S. Yong
This article proposes and investigates three methods for avoiding kinematic singularities in a teaching-playback robot manipulator system:
1. Nonredundancy singularity avoidance (NRSA) and redundancy singularity avoidance (RSA) modify the Jacobian matrix to reduce both position and orientation errors or prioritize position error reduction.
2. Point-to-point singularity avoidance (PTPSA) moves the end-effector through singular regions via joint-interpolated control without maintaining position and orientation.
Experimental case studies evaluate the three methods when the end-effector approaches wrist and shoulder singularities. Results show the methods effectively avoid singularities and enhance robot capability for industrial automation.
Engineering Research Publication
Best International Journals, High Impact Journals,
International Journal of Engineering & Technical Research
ISSN : 2321-0869 (O) 2454-4698 (P)
www.erpublication.org
Auto mobile vehicle direction in road traffic using artificial neural networkscsandit
This document presents a study on using an artificial neural network to analyze automobile vehicle routing in traffic conditions. Specifically, it aims to select the optimal route given inputs like distance, traffic volume, number of signals, road conditions and travel time. A backpropagation neural network with 5 input nodes, 10 hidden nodes and 5 output nodes representing 5 potential routes is created. The network is trained on 180 combinations of the 5 input parameters to learn the best route. Testing shows the backpropagation network achieves 91.2% accuracy in route selection, outperforming a network without backpropagation. The study concludes the network provides an effective approach for dynamic vehicle routing analysis, though some assumptions could be improved.
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKScsandit
So far Most of the current work on this area deals with traffic volume prediction during peak hours and the reasons behind accidents only. This work presents the analysis of automobile vehicle directing in various traffic flow conditions using Artificial neural network architecture.
Now a days, due to unprecedented increase in automobile vehicular traffic especially in metro-Politian cities, it has become highly imperative that we must choose an optimum road route in accordance with our requirements. The requirements are : volume of the traffic, Distance
between source and destination, no of signals in between the source and destination, the nature of the road condition , fuel consumption and Travel Timing. Artificial Neural networks, a soft computing technique, modeled after brain biological neuron functioning, helps to obtain the
required road way or route as per the training given to it. Here we make use of Back propagation network, which changes the weights value of the hidden layers, thereby activation function value which fires the neuron to get the required output.
The document outlines the course structure and content for CE 401: Discrete Mathematics. The course covers topics such as predicate calculus, fuzzy sets, group theory, lattices, Boolean algebra, and graph theory. It is taught over 3 lectures and has an internal assessment worth 30 marks and an end semester exam worth 70 marks. Reference books for the course are also listed.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document proposes using fuzzy logic to develop a collision avoidance system for trains. It describes fuzzy logic and how it can handle imprecise data and model nonlinear functions. The proposed system would use inputs like track vibrations and frequency to determine train distance and speed. It would compare the inputs to predetermined rules and provide outputs to control train speed. Examples show it could determine if a train should maintain speed, stop immediately, or increase speed based on the input conditions and rules. Fuzzy logic allows for a simple, intuitive approach to train collision avoidance.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
Smart element aware gate controller for intelligent wheeled robot navigationIJECEIAES
This document presents a modified neuro-controller mechanism for controlling the navigation of an indoor mobile robot. The proposed mechanism uses a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. The MEEG controller is able to estimate trajectory and overcome rigid and dynamic barriers intelligently. It was implemented on a Khepera IV mobile robot. Practical results showed the proposed mechanism was more efficient than MENN in providing the shortest distance to reach the goal with maximum velocity, minimizing the error rate by 58.33%. The document describes the system architecture, proposed neuro-controller, training algorithm for the MEEG, and presents analysis of sensor data and practical results.
Artificial Neural Network based Mobile Robot NavigationMithun Chowdhury
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
Identification and Control of Three-Links Electrically Driven Robot Arm Using...Waqas Tariq
This paper uses a fuzzy neural network (FNN) structure for identifying and controlling nonlinear dynamic systems such three links robot arm. The equation of motion for three links robot arm derived using Lagrange’s equation. This equation then combined with the equations of motion for dc. servo motors which actuated the robot. For the control problem, we present the forward and inverse adaptive control approaches using the FNN. Computer simulation is performed to view the results for identification and control
Navigation and Trajectory Control for Autonomous Robot/Vehicle (mechatronics)Mithun Chowdhury
The document is a presentation about navigation and trajectory control for autonomous vehicles. It was presented by two students from the University of Trento in Italy.
The presentation introduces mobile robot design considerations including the interrelation between tasks, environments, kinematic models, path/trajectory planning, and high-level and low-level control. It explains that the robot task and environment must be identified first and the kinematic model selected based on this. Path planning is then needed to generate admissible trajectories that satisfy the kinematic constraints. High-level control executes tasks and trajectories while low-level control handles velocity commands.
It also explains concepts like holonomic and non-holonomic constraints, accessibility spaces, and maneuvers
The document proposes a master's thesis to develop methods for measuring a driver's situational awareness during the transition from highly automated driving to manual control. It involves using sensors like eye trackers and cameras to recognize driver activities and detect their level of attention. Features will be extracted from eye and head movements to calculate a measure of situational awareness. A driving simulator will be used along with sensors to classify activities and evaluate how quickly drivers can resume control. The goal is to help vehicles determine if drivers are ready to manually drive during transitions from automated to manual modes.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses modeling DC servo motors using artificial neural networks (ANNs). It contains the following key points:
1. ANNs are a computational intelligence technique that can be used to model nonlinear systems like DC servo motors by learning from input-output data. ANNs have an interconnected structure that allows them to learn complex relationships.
2. A DC servo motor has electrical and mechanical components that can be modeled, including resistance, inductance, inertia, damping, input voltage, back EMF, and angular position/speed. Nonlinear effects like saturation and dead zones also need to be accounted for in the model.
3. The paper presents a motor model developed using ANN techniques to mimic the behavior of
Recognition and classification of human motionHutami Endang
This document describes research on recognizing and classifying human motions using hidden Markov models for building a motion database. Key points:
1) Human motions are extracted using bilateral control to obtain both position and force information.
2) Extracted motions are modeled as states defined by constant velocity segments and applied force vectors.
3) A real-time motion search method is proposed using hidden Markov models and the Viterbi algorithm to recognize motions as they occur based on velocity and force features.
4) The method is intended to allow a robot to accurately assist humans by recognizing their intended motions from a database in real-time using both kinematic and force information.
Combining Neural Network and Firefly Algorithm to Predict Stock Price in Tehr...Editor IJCATR
In the present research, prediction of stock price index in Tehran stock exchange by using neural
networks and firefly algorithm in chaotic behavior of price index stock exchange are studied. Two data sets
are selected for neural network input. Various breaks of index and macro economic factors are considered
as independent variables. Also, firefly algorithm is used to [redict price index in next week. The results of
research show that combining neural networks and firefly optimization algorithm has better performance
than neural network to predict the price index. In addition, acceptable value of error-sequre means for
network error in test data show that there are chaotic mevements in behaviour of price index.
CAPSULE NETWORK PERFORMANCE WITH AUTONOMOUS NAVIGATIONijaia
This paper proposes a Capsules Exploration Module (Caps-EM) that uses a Capsule Networks architecture paired with an Advantage Actor Critic algorithm for autonomous agent navigation. The Caps-EM is tested in sparse reward environments from the game ViZDoom and achieves better performance than previous approaches that used intrinsic reward modules, requiring fewer parameters and training time. Specifically, Caps-EM uses 44% and 83% fewer parameters and achieves 1141% and 437% average time improvement over the Intrinsic Curiosity Module and Depth-Augmented Curiosity Module respectively for converging navigation policies across test scenarios.
This document compares the performance of two neural network architectures, multi-layer perceptron (MLP) and radial basis function (RBF) networks, on a face recognition system. It trains MLP networks using different variants of the backpropagation algorithm and compares the results to RBF networks. The document finds that RBF networks provide better generalization performance compared to backpropagation algorithms and have faster training times, making them more suitable for face recognition.
CAPSULE NETWORK PERFORMANCE WITH AUTONOMOUS NAVIGATIONgerogepatton
Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios. In real world navigation, rewards external to agents may be rare. In turn, reinforcement learning algorithms can struggle to form meaningful policy functions. This paper’s approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage Actor Critic algorithm. Other approaches for navigating sparse environments require intrinsic reward generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM). CapsEM uses a more compact architecture without need for intrinsic rewards. Tested using ViZDoom, the CapsEM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and DACM, respectively, for converging to a policy function across "My Way Home" scenarios
Summer Internships at the Center for Advanced Research The Center ...butest
The Center for Advanced Research at PricewaterhouseCoopers is offering summer internships in 2010 in several areas including user modeling, user interface design, complex event analysis, information extraction, time-series data analysis, and healthcare data mining. The internships will involve hands-on work developing algorithms, analyzing data, and interacting with live data to identify patterns and events. Candidates must be currently enrolled in a relevant graduate program and have experience in the focus area. Programming skills and knowledge of languages like Java, Python, and SQL are required for most positions.
This document discusses classifying network traffic flows into quality of service (QoS) classes using unsupervised machine learning and K-nearest neighbor clustering. It first reviews previous work in traffic classification. It then uses self-organizing maps and K-means clustering as unsupervised methods to identify three inherent traffic classes - transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier is then evaluated and found to have a low error rate of around 2% for test data, significantly better than a minimum mean distance classifier with 7% error.
This document provides information about the Postgraduate Diploma/MSc in Autonomous Intelligent Systems for the 2009-2010 academic year at Aberystwyth University. The course consists of 7 modules taken over 2 parts. Part 1 involves 6 modules covering topics like intelligent systems, machine learning, and autonomous systems. Part 2 is a dissertation module. Students must complete 180 credits to receive an MSc, including passing parts 1 and 2. The course can also be taken part-time over 2 years. Modules are intensive and involve both lectures and practical work. Assessment includes coursework, presentations, and exams.
CP2083 Introduction to Artificial Intelligencebutest
This document provides information about the CP2083 Introduction to Artificial Intelligence module offered in 2003/2004 at the School of Computing and Information Technology. The module is a level 2, 15 credit module that introduces core AI algorithms including knowledge representation, problem space search techniques, machine learning, neural networks, genetic algorithms, and software agents. It will be taught on Thursdays from 9am to 1pm and assessed through a programming assignment worth 50% and a closed book exam worth 50%. The module is aimed at providing students with an understanding of modern AI algorithms and the ability to construct an AI system using library routines.
The document outlines the course structure and content for CE 401: Discrete Mathematics. The course covers topics such as predicate calculus, fuzzy sets, group theory, lattices, Boolean algebra, and graph theory. It is taught over 3 lectures and has an internal assessment worth 30 marks and an end semester exam worth 70 marks. Reference books for the course are also listed.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document proposes using fuzzy logic to develop a collision avoidance system for trains. It describes fuzzy logic and how it can handle imprecise data and model nonlinear functions. The proposed system would use inputs like track vibrations and frequency to determine train distance and speed. It would compare the inputs to predetermined rules and provide outputs to control train speed. Examples show it could determine if a train should maintain speed, stop immediately, or increase speed based on the input conditions and rules. Fuzzy logic allows for a simple, intuitive approach to train collision avoidance.
Optimized Robot Path Planning Using Parallel Genetic Algorithm Based on Visib...IJERA Editor
An analysis is made for optimized path planning for mobile robot by using parallel genetic algorithm. The
parallel genetic algorithm (PGA) is applied on the visible midpoint approach to find shortest path for mobile
robot. The hybrid ofthese two algorithms provides a better optimized solution for smooth and shortest path for
mobile robot. In this problem, the visible midpoint approach is used to make the effectiveness for avoiding
local minima. It gives the optimum paths which are always consisting on free trajectories. But the
proposedhybrid parallel genetic algorithm converges very fast to obtain the shortest route from source to
destination due to the sharing of population. The total population is partitioned into a number subgroups to
perform the parallel GA. The master thread is the center of information exchange and making selection with
fitness evaluation.The cell to cell crossover makes the algorithm significantly good. The problem converges
quickly with in a less number of iteration.
Smart element aware gate controller for intelligent wheeled robot navigationIJECEIAES
This document presents a modified neuro-controller mechanism for controlling the navigation of an indoor mobile robot. The proposed mechanism uses a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. The MEEG controller is able to estimate trajectory and overcome rigid and dynamic barriers intelligently. It was implemented on a Khepera IV mobile robot. Practical results showed the proposed mechanism was more efficient than MENN in providing the shortest distance to reach the goal with maximum velocity, minimizing the error rate by 58.33%. The document describes the system architecture, proposed neuro-controller, training algorithm for the MEEG, and presents analysis of sensor data and practical results.
Artificial Neural Network based Mobile Robot NavigationMithun Chowdhury
This document presents a neural network based navigation system for mobile robots. It uses an artificial neural network (ANN) trained with Backpropagation Through Time (BPTT) to plan paths and navigate around obstacles. The input to the ANN is the state of the robot described using polar coordinates relative to the target position and orientation. Obstacles are also included as inputs by dividing the area in front of the robot into regions. The cost function for training is extended with a potential field to repel the robot from obstacles. Simulation results showed the robot could successfully navigate a maze and reach the target while avoiding multiple obstacles.
Identification and Control of Three-Links Electrically Driven Robot Arm Using...Waqas Tariq
This paper uses a fuzzy neural network (FNN) structure for identifying and controlling nonlinear dynamic systems such three links robot arm. The equation of motion for three links robot arm derived using Lagrange’s equation. This equation then combined with the equations of motion for dc. servo motors which actuated the robot. For the control problem, we present the forward and inverse adaptive control approaches using the FNN. Computer simulation is performed to view the results for identification and control
Navigation and Trajectory Control for Autonomous Robot/Vehicle (mechatronics)Mithun Chowdhury
The document is a presentation about navigation and trajectory control for autonomous vehicles. It was presented by two students from the University of Trento in Italy.
The presentation introduces mobile robot design considerations including the interrelation between tasks, environments, kinematic models, path/trajectory planning, and high-level and low-level control. It explains that the robot task and environment must be identified first and the kinematic model selected based on this. Path planning is then needed to generate admissible trajectories that satisfy the kinematic constraints. High-level control executes tasks and trajectories while low-level control handles velocity commands.
It also explains concepts like holonomic and non-holonomic constraints, accessibility spaces, and maneuvers
The document proposes a master's thesis to develop methods for measuring a driver's situational awareness during the transition from highly automated driving to manual control. It involves using sensors like eye trackers and cameras to recognize driver activities and detect their level of attention. Features will be extracted from eye and head movements to calculate a measure of situational awareness. A driving simulator will be used along with sensors to classify activities and evaluate how quickly drivers can resume control. The goal is to help vehicles determine if drivers are ready to manually drive during transitions from automated to manual modes.
Black-box modeling of nonlinear system using evolutionary neural NARX modelIJECEIAES
Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses modeling DC servo motors using artificial neural networks (ANNs). It contains the following key points:
1. ANNs are a computational intelligence technique that can be used to model nonlinear systems like DC servo motors by learning from input-output data. ANNs have an interconnected structure that allows them to learn complex relationships.
2. A DC servo motor has electrical and mechanical components that can be modeled, including resistance, inductance, inertia, damping, input voltage, back EMF, and angular position/speed. Nonlinear effects like saturation and dead zones also need to be accounted for in the model.
3. The paper presents a motor model developed using ANN techniques to mimic the behavior of
Recognition and classification of human motionHutami Endang
This document describes research on recognizing and classifying human motions using hidden Markov models for building a motion database. Key points:
1) Human motions are extracted using bilateral control to obtain both position and force information.
2) Extracted motions are modeled as states defined by constant velocity segments and applied force vectors.
3) A real-time motion search method is proposed using hidden Markov models and the Viterbi algorithm to recognize motions as they occur based on velocity and force features.
4) The method is intended to allow a robot to accurately assist humans by recognizing their intended motions from a database in real-time using both kinematic and force information.
Combining Neural Network and Firefly Algorithm to Predict Stock Price in Tehr...Editor IJCATR
In the present research, prediction of stock price index in Tehran stock exchange by using neural
networks and firefly algorithm in chaotic behavior of price index stock exchange are studied. Two data sets
are selected for neural network input. Various breaks of index and macro economic factors are considered
as independent variables. Also, firefly algorithm is used to [redict price index in next week. The results of
research show that combining neural networks and firefly optimization algorithm has better performance
than neural network to predict the price index. In addition, acceptable value of error-sequre means for
network error in test data show that there are chaotic mevements in behaviour of price index.
CAPSULE NETWORK PERFORMANCE WITH AUTONOMOUS NAVIGATIONijaia
This paper proposes a Capsules Exploration Module (Caps-EM) that uses a Capsule Networks architecture paired with an Advantage Actor Critic algorithm for autonomous agent navigation. The Caps-EM is tested in sparse reward environments from the game ViZDoom and achieves better performance than previous approaches that used intrinsic reward modules, requiring fewer parameters and training time. Specifically, Caps-EM uses 44% and 83% fewer parameters and achieves 1141% and 437% average time improvement over the Intrinsic Curiosity Module and Depth-Augmented Curiosity Module respectively for converging navigation policies across test scenarios.
This document compares the performance of two neural network architectures, multi-layer perceptron (MLP) and radial basis function (RBF) networks, on a face recognition system. It trains MLP networks using different variants of the backpropagation algorithm and compares the results to RBF networks. The document finds that RBF networks provide better generalization performance compared to backpropagation algorithms and have faster training times, making them more suitable for face recognition.
CAPSULE NETWORK PERFORMANCE WITH AUTONOMOUS NAVIGATIONgerogepatton
Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios. In real world navigation, rewards external to agents may be rare. In turn, reinforcement learning algorithms can struggle to form meaningful policy functions. This paper’s approach Capsules Exploration Module (Caps-EM) pairs a CapsNets architecture with an Advantage Actor Critic algorithm. Other approaches for navigating sparse environments require intrinsic reward generators, such as the Intrinsic Curiosity Module (ICM) and Augmented Curiosity Modules (ACM). CapsEM uses a more compact architecture without need for intrinsic rewards. Tested using ViZDoom, the CapsEM uses 44% and 83% fewer trainable network parameters than the ICM and Depth-Augmented Curiosity Module (D-ACM), respectively, for 1141% and 437% average time improvement over the ICM and DACM, respectively, for converging to a policy function across "My Way Home" scenarios
Summer Internships at the Center for Advanced Research The Center ...butest
The Center for Advanced Research at PricewaterhouseCoopers is offering summer internships in 2010 in several areas including user modeling, user interface design, complex event analysis, information extraction, time-series data analysis, and healthcare data mining. The internships will involve hands-on work developing algorithms, analyzing data, and interacting with live data to identify patterns and events. Candidates must be currently enrolled in a relevant graduate program and have experience in the focus area. Programming skills and knowledge of languages like Java, Python, and SQL are required for most positions.
This document discusses classifying network traffic flows into quality of service (QoS) classes using unsupervised machine learning and K-nearest neighbor clustering. It first reviews previous work in traffic classification. It then uses self-organizing maps and K-means clustering as unsupervised methods to identify three inherent traffic classes - transactional, bulk data transfer, and interactive applications. The K-nearest neighbor classifier is then evaluated and found to have a low error rate of around 2% for test data, significantly better than a minimum mean distance classifier with 7% error.
This document provides information about the Postgraduate Diploma/MSc in Autonomous Intelligent Systems for the 2009-2010 academic year at Aberystwyth University. The course consists of 7 modules taken over 2 parts. Part 1 involves 6 modules covering topics like intelligent systems, machine learning, and autonomous systems. Part 2 is a dissertation module. Students must complete 180 credits to receive an MSc, including passing parts 1 and 2. The course can also be taken part-time over 2 years. Modules are intensive and involve both lectures and practical work. Assessment includes coursework, presentations, and exams.
CP2083 Introduction to Artificial Intelligencebutest
This document provides information about the CP2083 Introduction to Artificial Intelligence module offered in 2003/2004 at the School of Computing and Information Technology. The module is a level 2, 15 credit module that introduces core AI algorithms including knowledge representation, problem space search techniques, machine learning, neural networks, genetic algorithms, and software agents. It will be taught on Thursdays from 9am to 1pm and assessed through a programming assignment worth 50% and a closed book exam worth 50%. The module is aimed at providing students with an understanding of modern AI algorithms and the ability to construct an AI system using library routines.
Map the life cycle of an item of clothing, consider the energy ...butest
The document traces the life cycle of a cotton hoodie from production to disposal. It begins with cotton farming in China, where ethical issues arise from the lack of free market prices set by farmers. The cotton is then manufactured and packaged in China, using significant amounts of energy, water and creating waste. It is then shipped by container vessel to the US, burning over 100 gallons of fuel per mile traveled. The hoodie is then purchased, laundered and dried frequently by the owner in the US, consuming large amounts of electricity, water and hastening its obsolescence. Upon disposal, it may be donated or recycled, though both require further transportation and resource use to extend its life cycle.
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learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcsandit
This paper reports results of artificial neural network for robot navigation tasks. Machine
learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”.
In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer
Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the
PNN is the best classification accuracy with 99,635% accuracy using same dataset.
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LEARNING OF ROBOT NAVIGATION TASKS BY PROBABILISTIC NEURAL NETWORKcscpconf
This paper reports results of artificial neural network for robot navigation tasks. Machine learning methods have proven usability in many complex problems concerning mobile robots
control. In particular we deal with the well-known strategy of navigating by “wall-following”. In this study, probabilistic neural network (PNN) structure was used for robot navigation tasks.
The PNN result was compared with the results of the Logistic Perceptron, Multilayer Perceptron, Mixture of Experts and Elman neural networks and the results of the previous
studies reported focusing on robot navigation tasks and using same dataset. It was observed the PNN is the best classification accuracy with 99,635% accuracy using same dataset.
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Reactive Navigation of Autonomous Mobile Robot Using Neuro-Fuzzy SystemWaqas Tariq
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Universal Artificial Intelligence for Intelligent Agents: An Approach to Supe...IOSR Journals
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Optimally Learnt, Neural Network Based Autonomous Mobile Robot Navigation SystemIDES Editor
Neural network based systems have been used in
past years for robot navigation applications because of their
ability to learn human expertise and to utilize this knowledge
to develop autonomous navigation strategies. In this paper,
neural based systems are developed for mobile robot reactive
navigation. The proposed systems transform sensors’ input to
yield wheel velocities. Novel algorithm is proposed for optimal
training of neural network. With a view to ascertain the efficacy
of proposed system; developed neural system’s performance
is compared to other neural and fuzzy based approaches.
Simulation results show effectiveness of proposed system in
all kind of obstacle environments.
Mobile robot controller using novel hybrid system IJECEIAES
Hybrid neuro-fuzzy controller is one of the techniques that is used as a tool to control a mobile robot in unstructured environment. In this paper a novel neuro-fuzzy technique is proposed in order to tackle the problem of mobile robot autonomous navigation in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the artificial neural network instead of the fuzzified engine then the output from it is processed using adaptive inference engine and defuzzification engine. In this approach, the real processing time is reduced that is increase the mobile robot response. The proposed neuro-fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Kinematics modeling of six degrees of freedom humanoid robot arm using impro...IJECEIAES
The robotic arm has functioned as an arm in the humanoid robot and is generally used to perform grasping tasks. Accordingly, kinematics modeling both forward and inverse kinematics is required to calculate the end-effector position in the cartesian space before performing grasping activities. This research presents the kinematics modeling of six degrees of freedom (6-DOF) robotic arm of the T-FLoW humanoid robot for the grasping mechanism of visual grasping systems on the robot operating system (ROS) platform and CoppeliaSim. Kinematic singularity is a common problem in the inverse kinematics model of robots, but. However, other problems are mechanical limitations and computational time. The work uses the homogeneous transformation matrix (HTM) based on the Euler system of the robot for the forward kinematics and demonstrates the capability of an improved damped least squares (I-DLS) method for the inverse kinematics. The I-DLS method was obtained by improving the original DLS method with the joint limits and clamping techniques. The I-DLS performs better than the original DLS during the experiments yet increases the calculation iteration by 10.95%, with a maximum error position between the endeffector and target positions in path planning of 0.1 cm.
Semi-Autonomous Control of a Multi-Agent Robotic System for Multi-Target Oper...Waqas Tariq
This document proposes a control method for a single-master multi-slave teleoperation system to control multiple cooperative mobile robots for multi-target missions. The control method includes a modified potential field-based leader-follower formation approach and a robot-target pairing method. The pairing method uses an auction algorithm to optimally pair robots to targets. The robots are split into subteams based on the pairings and each subteam autonomously approaches its paired target while avoiding obstacles. Simulation studies demonstrate the effectiveness of this control method for multi-target operations.
The document discusses forward and inverse kinematics for humanoid robots. It presents an analytical solution to the forward and inverse kinematics problems for the Aldebaran NAO humanoid robot. The solution decomposes the robot into five independent kinematic chains (head, two arms, two legs). It uses the Denavit-Hartenberg method and solves a non-linear system of equations to find exact closed-form solutions. The implemented kinematics library allows real-time transformations between joint configurations and physical positions, enabling motions like balancing and tracking a moving ball.
Impact of initialization of a modified particle swarm optimization on coopera...IJECEIAES
Swarm robotic is well known for its flexibility, scalability and robustness that make it suitable for solving many real-world problems. Source searching which is characterized by complex operation due to the spatial characteristic of the source intensity distribution, uncertain searching environments and rigid searching constraints is an example of application where swarm robotics can be applied. Particle swarm optimization (PSO) is one of the famous algorithms have been used for source searching where its effectiveness depends on several factors. Improper parameter selection may lead to a premature convergence and thus robots will fail (i.e., low success rate) to locate the source within the given searching constraints. Additionally, target overshooting and improper initialization strategies may lead to a nonoptimal (i.e., take longer time to converge) target searching. In this study, a modified PSO and three different initializations strategies (i.e., random, equidistant and centralized) were proposed. The findings shown that the proposed PSO model successfully reduce the target overshooting by choosing optimal PSO parameters and has better convergence rate and success rate compared to the benchmark algorithms. Additionally, the findings also indicate that the random initialization give better searching success compared to equidistant and centralize initialization.
Autonomous system to control a mobile robotjournalBEEI
This paper presents an ongoing effort to control a mobile robot in unstructured environment. Obstacle avoidance is an important task in the field of robotics, since the goal of autonomous robot is to reach the destination without collision. Several algorithms have been proposed for obstacle avoidance, having drawbacks and benefits. In this paper, the fuzzy controller is used to tackle the problem of mobile robot autonomous navigation in unstructured environment. The objective is to make the robot move along a collision free trajectory until it reaches its target. The proposed approach uses the fuzzified, adaptive inference engine and defuzzification engine. Also number of linguistic labels is optimized for the input of the mobile robot in order to reduce computational time for real-time applications. The proposed fuzzy controller is evaluated subjectively and objectively with other approaches and also the processing time is taken in consideration.
This document introduces the fuzzy model reference learning control (FMRLC) method. FMRLC uses a reference model to provide feedback to modify the membership functions of a fuzzy controller. This allows the closed-loop system to behave like the reference model and achieve the desired performance. The effectiveness of FMRLC is demonstrated through its application to rocket velocity control and robot manipulator control. FMRLC can achieve high performance learning control for nonlinear, time-varying systems.
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1. Towards Machine Learning of Motor Skills
Jan Peters1,2 , Stefan Schaal2 and Bernhard Sch¨lkopf1
o
(1) Max-Planck Institute for Biological Cybernetics, Spemannstr. 32, 72074 T¨bingen
u
(2) University of Southern California, 3641 Watt Way, Los Angeles, CA 90802
Abstract. Autonomous robots that can adapt to novel situations has
been a long standing vision of robotics, artificial intelligence, and cog-
nitive sciences. Early approaches to this goal during the heydays of ar-
tificial intelligence research in the late 1980s, however, made it clear
that an approach purely based on reasoning or human insights would
not be able to model all the perceptuomotor tasks that a robot should
fulfill. Instead, new hope was put in the growing wake of machine learn-
ing that promised fully adaptive control algorithms which learn both by
observation and trial-and-error. However, to date, learning techniques
have yet to fulfill this promise as only few methods manage to scale
into the high-dimensional domains of manipulator robotics, or even the
new upcoming trend of humanoid robotics, and usually scaling was only
achieved in precisely pre-structured domains. In this paper, we inves-
tigate the ingredients for a general approach to motor skill learning in
order to get one step closer towards human-like performance. For doing
so, we study two major components for such an approach, i.e., firstly, a
theoretically well-founded general approach to representing the required
control structures for task representation and execution and, secondly,
appropriate learning algorithms which can be applied in this setting.
1 Introduction
Despite an increasing number of motor skills exhibited by manipulator and hu-
manoid robots, the general approach to the generation of such motor behaviors
has changed little over the last decades [2,11]. The roboticist models the task
as accurately as possible and uses human understanding of the required motor
skills in order to create the desired robot behavior as well as to eliminate all
uncertainties of the environment. In most cases, such a process boils down to
recording a desired trajectory in a pre-structured environment with precisely
placed objects. If inaccuracies remain, the engineer creates exceptions using hu-
man understanding of the task. While such highly engineered approaches are
feasible in well-structured industrial or research environments, it is obvious that
if robots should ever leave factory floors and research environments, we will need
to reduce or eliminate the strong reliance on hand-crafted models of the envi-
ronment and the robots exhibited to date. Instead, we need a general approach
which allows us to use compliant robots designed for interaction with less struc-
tured and uncertain environments in order to reach domains outside industry.
2. Such an approach cannot solely rely on human knowledge but instead has to be
acquired and adapted from data generated both by human demonstrations of
the skill as well as trial and error of the robot.
The tremendous progress in machine learning over the last decades offers us
the promise of less human-driven approaches to motor skill acquisition. However,
despite offering the most general way of thinking about data-driven acquisition
of motor skills, generic machine learning techniques, which do not rely on an
understanding of motor systems, often do not scale into the domain of manip-
ulator or humanoid robotics due to the high domain dimensionality. Therefore,
instead of attempting an unstructured, monolithic machine learning approach
to motor skill aquisition, we need to develop approaches suitable for this par-
ticular domain with the inherent problems of task representation, learning and
execution addressed separately in a coherent framework employing a combina-
tion of imitation, reinforcement and model learning in order to cope with the
complexities involved in motor skill learning. The advantage of such a concerted
approach is that it allows the separation of the main problems of motor skill
acquisition, refinement and control. Instead of either having an unstructured,
monolithic machine learning approach or creating hand-crafted approaches with
pre-specified trajectories, we are capable of aquiring skills, represented as poli-
cies, from demonstrations and refine them using trial and error. Using learning-
based approaches for control, we can achieve accurate control without needing
accurate models of the complete system.
2 Foundations for Motor Skill Learning
The principal objective of this paper is to find the foundations for a general
framework for representing, learning and executing motor skills for robotics. As
can be observed from this question, the major goal of this paper requires three
building blocks, i.e., (i) appropriate representations for movements, (ii) learning
algorithms which can be applied to these representations and (iii) a transforma-
tion which allows the execution of the kinematic policies in the respective task
space on robots.
2.1 Essential Components
We address the three essential components, i.e., representation, learning and ex-
ecution. In this section, we briefly outline the underlying fundamental concepts.
Representation. For the representation of motor skills, we can rely on the insight
that humans, while being capable of performing a large variety of complicated
movements, restrict themselves to a smaller amount of primitive motions [10]. As
suggested by Ijspeert et al. [4,3], such primitive movements can be represented by
nonlinear dynamic systems. We can represent these in the differential constraint
form given by
Aθi (xi , xi , t)¨ = bθi (xi , xi , t),
˙ x ˙ (1)
3. Episodic Learning from Real-time Learning from
Long-Term Rewards Immediate Rewards
Perceptual Motor Primitive Control Task Robot
Triggering Representation Execution System
Fig. 1. This figure illustrates our general approach to motor skill learning by dividing
it into motor primitive and a motor control component. For the task execution, fast
policy learning methods based on observable error need to be employed while the task
learning is based on slower episodic learning.
where i ∈ N is the index of the motor primitive in a library of movements, θi ∈
RL denote the parameters of the primitive i, t denotes time and xi ,xi ,¨i ∈ Rn
˙ x
denote positions, velocities and accelerations of the dynamic system, respectively.
Learning. Learning basic motor skills1 is achieved by adapting the parameters
θi of motor primitive i. The high dimensionality of our domain prohibits the ex-
ploration of the complete space of all admissible motor behaviors, rendering the
application of machine learning techniques which require exhaustive exploration
impossible. Instead, we have to rely on a combination of supervised and rein-
forcement learning in order to aquire motor skills where the supervised learning
is used in order to obtain the initialization of the motor skill while reinforce-
ment learning is used in order to improve it. Therefore, the aquisition of a novel
motor task consists out of two phases,i.e., the ‘learning robot’ attempts to repro-
duce the skill acquired through supervised learning and improve the skill from
experience by trial-and-error, i.e., through reinforcement learning.
Execution. The execution of motor skills adds another level of complexity. It
requires that a mechanical system
u = M (q, q, t)¨ + F (q, q, t),
˙ q ˙ (2)
with a mapping xi = f i (q, q, t) can be forced to execute each motor primitive
˙
Ai xi = bi in order to fulfill the skill. The motor primitive can be viewed as a
¨
mechanical constraint acting upon the system, enforced through accurate com-
putation of the required forces based on analytical models. However, in most
cases it is very difficult to obtain accurate models of the mechanical system.
Therefore it can be more suitable to find a policy learning approach which re-
places the control law based on the hand-crafted rigid body model. In this paper,
1
Learning by sequencing and parallelization of the motor primitives will be treated
in future work.
4. we will follow this approach which forms the basis for understanding motor skill
learning.
2.2 Resulting Approach
As we have outlined during the discussion of our objective and its essential com-
ponents, we require an appropriate general motor skill framework which allows
us to separate the desired task-space movement generation (represented by the
motor primitives) from movement control in the respective actuator space. Based
on the understanding of this transformation from an analytical point of view on
robotics, we presente a learning framework for task execution in operational
space. For doing so, we have to consider two components, i.e., we need to deter-
mine how to learn the desired behavior represented by the motor primitives as
well as the execution represented by the transformation of the motor primitives
into motor commands. We need to develop scalable learning algorithms which
are both appropriate and efficient when used with the chosen general motor skill
learning architecture. Furthermore, we require algorithms for fast immediate
policy learning for movement control based on instantly observable rewards in
order to enable the system to cope with real-time improvement during the exe-
cution. The learning of the task itself on the other hand requires the learning of
policies which define the long-term evolution of the task, i.e., motor primitives,
which are learned on a trial-by-trial basis with episodic improvement using a
teacher for demonstration and reinforcement learning for self-improvement. The
resulting general concept underlying this paper is illustrated in Figure 1.
2.3 Novel Learning Algorithms
As outlined before, we need two different styles of policy learning algorithms,
i.e., methods for long-term reward optimization and methods for immediate
improvement. Thus, we have developed two different classes of algorithms, i.e.,
the Natural Actor-Critic and the Reward-Weighted Regression.
Natural Actor-Critic. The Natural Actor-Critic algorithms [8,9] are the fastest
policy gradient methods to date and “the current method of choice” [1]. They
rely on the insight that we need to maximize the reward while keeping the loss
of experience constant, i.e., we need to measure the distance between our cur-
rent path distribution and the new path distribution created by the policy. This
distance can be measured by the Kullback-Leibler divergence and approximated
using the Fisher information metric resulting in a natural policy gradient ap-
proach. This natural policy gradient has a connection to the recently introduced
compatible function approximation, which allows to obtain the Natural Actor-
Critic. Interestingly, earlier Actor-Critic approaches can be derived from this
new approach. In application to motor primitive learning, we can demonstrate
that the Natural Actor-Critic outperforms both finite-difference gradients as well
as ‘vanilla’ policy gradient methods with optimal baselines.
5. (a) 3 DoF Robot Arm (b) Tracking Performance
0.16
Hand coordinate x2
0.14
0.12
0.1
0.08
0.06 learned
desired
0.04
0.44 0.48 0.52 0.56
(c) SARCOS Master Hand coordinate x1
Robot Arm
(d) Optimal vs Learned Motor Command
60
a11
Taskspace motor
50
commands =1
40
30 a 2
1
20
10 learned
0 a13 optimal
Time t
-10 0 0.5 1 1.5 2
Fig. 2. Systems and results of evaluations for learning operational space control: (a)
screen shot of the 3 DOF arm simulator, (c) Sarcos robot arm, used as simulated
system and for actual robot evaluations in progress. (b) Tracking performance for a
planar figure-8 pattern for the 3 DOF arm, and (d) comparison between the analytically
obtained optimal control commands in comparison to the learned ones for one figure-8
cycle of the 3DOF arm.
Reward-Weighted Regression. In contrast to Natural Actor-Critic algorithms,
the Reward-Weighted Regression algorithm [6,5,7] focuses on immediate reward
improvement and employs an adaptation of the expectation maximization (EM)
algorithm for reinforcement learning instead of a gradient based approach. The
key difference here is that when using immediate rewards, we can learn from
our actions directly, i.e., use them as training examples similar to a supervised
learning problem with a higher priority for samples with a higher reward. Thus,
this problem is a reward-weighted regression problem, i.e., it has a well-defined
solution which can be obtained using established regression techniques. While
we have given a more intuitive explanation of this algorithm, it corresponds to a
properly derived maximization-maximization (MM) algorithm which maximizes
a lower bound on the immediate reward similar to an EM algorithm. Our appli-
cations show that it scales to high dimensional domains and learns a good policy
without any imitation of a human teacher.
3 Robot Application
The general setup presented in this paper can be applied in robotics using an-
alytical models as well as the presented learning algorithms. The applications
6. (a) Performance (b) Teach in (c) Initial re- (d) Improved re-
x 10 ofthe system by Imitation produced motion produced motion
5
Performance J(θ)
0
-2
-4
-6
-8
-10
0 100 200 300 400
Episodes
Fig. 3. This figure shows (a) the performance of a baseball swing task when using the
motor primitives for learning. In (b), the learning system is initialized by imitation
learning, in (c) it is initially failing at reproducing the motor behavior, and (d) after
several hundred episodes exhibiting a nicely learned batting.
presented in this paper include motor primitive learning and operational space
control.
3.1 Learning Operational Space Control
Operational space control is one of the most general frameworks for obtaining
task-level control laws in robotics. In this paper, we present a learning framework
for operational space control which is a result of a reformulation of operational
space control as a general point-wise optimal control framework and our insights
into immediate reward reinforcement learning. While the general learning of op-
erational space controllers with redundant degrees of freedom is non-convex and
thus global supervised learning techniques cannot be applied straightforwardly,
we can gain two insights, i.e., that the problem is locally convex and that our
point-wise cost function allows us to ensure global consistency among the lo-
cal solutions. We show that this can yield the analytically determined optimal
solution for simulated three degrees of freedom arms where we can sample the
state-space sufficiently. Similarly, we can show the framework works well for sim-
ulations of the both three and seven degrees of freedom robot arms as presented
in Figure 2.
3.2 Motor Primitive Improvement by Reinforcement Learning
The main application of our long-term improvement framework is the optimiza-
tion of motor primitives. Here, we follow essentially the previously outlined idea
of acquiring an initial solution by supervised learning and then using reinforce-
ment learning for motor primitive improvement. For this, we demonstrate both
comparisons of motor primitive learning with different policy gradient methods,
i.e., finite difference methods, ‘vanilla’ policy gradient methods and the Natural
Actor-Critic, as well as an application of the most successful method, the Nat-
ural Actor-Critic to T-Ball learning on a physical, anthropomorphic SARCOS
Master Arm, see Figure 3.
7. 4 Conclusion
In conclusion, in this paper, we have preseted a general framework for learn-
ing motor skills which is based on a thorough, analytically understanding of
robot task representation and execution. We have introduced two classes of
novel reinforcement learning methods, i.e., the Natural Actor-Critic and the
Reward-Weighted Regression algorithm. We demonstrate the efficiency of these
reinforcement learning methods in the application of learning to hit a baseball
with an anthropomorphic robot arm on a physical SARCOS master arm using
the Natural Actor-Critic, and in simulation for the learning of operational space
with reward-weighted regression.
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