Self-attentive vision in evolutionary robotics
- Authors: Botha, Bouwer
- Date: 2024-04
- Subjects: Evolutionary robotics , Robotics , Neural networks (Computer science)
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/63628 , vital:73566
- Description: The autonomy of a robot refers to its ability to achieve a task in an environment with minimal human supervision. This may require autonomous solutions to be able to perceive their environment to inform their decisions. An inexpensive and highly informative way that robots can perceive the environment is through vision. The autonomy of a robot is reliant on the quality of the robotic controller. These controllers are the software interface between the robot and environment that determine the actions of the robot based on the perceived environment. Controllers are typically created using manual programming techniques, which become progressively more challenging with increasing complexity of both the robot and task. An alternative to manual programming is the use of machine learning techniques such as those used by Evolutionary Robotics (ER). ER is an area of research that investigates the automatic creation of controllers. Instead of manually programming a controller, an Evolutionary Algorithms can be used to evolve the controller through repeated interactions with the task environment. Employing the ER approach on camera-based controllers, however, has presented problems for conventional ER methods. Firstly, existing architectures that are capable of automatically processing images, have a large number of trained parameters. These architectures over-encumber the evolutionary process due to the large search space of possible configurations. Secondly, the evolution of complex controllers needs to be done in simulation, which requires either: (a) the construction of a photo-realistic virtual environment with accurate lighting, texturing and models or (b) potential reduction of the controller capability by simplifying the problem via image preprocessing. Any controller trained in simulation also raises the inherent concern of not being able to transfer to the real world. This study proposes a new technique for the evolution of camera-based controllers in ER, that aims to address the highlighted problems. The use of self-attention is proposed to facilitate the evolution of compact controllers that are able to evolve specialized sets of task-relevant features in unprocessed images by focussing on important image regions. Furthermore, a new neural network-based simulation approach, Generative Neuro-Augmented Vision (GNAV), is proposed to simplify simulation construction. GNAV makes use of random data collected in a simple virtual environment and the real world. A neural network is trained to overcome the visual discrepancies between these two environments. GNAV enables a controller to be trained in a simple simulated environment that appears similar to the real environment, while requiring minimal human supervision. The capabilities of the new technique were demonstrated using a series of real-world navigation tasks based on camera vision. Controllers utilizing the proposed self-attention mechanism were trained using GNAV and transferred to a real camera-equipped robot. The controllers were shown to be able to perform the same tasks in the real world. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
- Authors: Botha, Bouwer
- Date: 2024-04
- Subjects: Evolutionary robotics , Robotics , Neural networks (Computer science)
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10948/63628 , vital:73566
- Description: The autonomy of a robot refers to its ability to achieve a task in an environment with minimal human supervision. This may require autonomous solutions to be able to perceive their environment to inform their decisions. An inexpensive and highly informative way that robots can perceive the environment is through vision. The autonomy of a robot is reliant on the quality of the robotic controller. These controllers are the software interface between the robot and environment that determine the actions of the robot based on the perceived environment. Controllers are typically created using manual programming techniques, which become progressively more challenging with increasing complexity of both the robot and task. An alternative to manual programming is the use of machine learning techniques such as those used by Evolutionary Robotics (ER). ER is an area of research that investigates the automatic creation of controllers. Instead of manually programming a controller, an Evolutionary Algorithms can be used to evolve the controller through repeated interactions with the task environment. Employing the ER approach on camera-based controllers, however, has presented problems for conventional ER methods. Firstly, existing architectures that are capable of automatically processing images, have a large number of trained parameters. These architectures over-encumber the evolutionary process due to the large search space of possible configurations. Secondly, the evolution of complex controllers needs to be done in simulation, which requires either: (a) the construction of a photo-realistic virtual environment with accurate lighting, texturing and models or (b) potential reduction of the controller capability by simplifying the problem via image preprocessing. Any controller trained in simulation also raises the inherent concern of not being able to transfer to the real world. This study proposes a new technique for the evolution of camera-based controllers in ER, that aims to address the highlighted problems. The use of self-attention is proposed to facilitate the evolution of compact controllers that are able to evolve specialized sets of task-relevant features in unprocessed images by focussing on important image regions. Furthermore, a new neural network-based simulation approach, Generative Neuro-Augmented Vision (GNAV), is proposed to simplify simulation construction. GNAV makes use of random data collected in a simple virtual environment and the real world. A neural network is trained to overcome the visual discrepancies between these two environments. GNAV enables a controller to be trained in a simple simulated environment that appears similar to the real environment, while requiring minimal human supervision. The capabilities of the new technique were demonstrated using a series of real-world navigation tasks based on camera vision. Controllers utilizing the proposed self-attention mechanism were trained using GNAV and transferred to a real camera-equipped robot. The controllers were shown to be able to perform the same tasks in the real world. , Thesis (MSc) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 2024
- Full Text:
- Date Issued: 2024-04
Artificial neural networks as simulators for behavioural evolution in evolutionary robotics
- Pretorius, Christiaan Johannes
- Authors: Pretorius, Christiaan Johannes
- Date: 2010
- Subjects: Neural networks (Computer science) , Robotics
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10462 , http://hdl.handle.net/10948/1476 , Neural networks (Computer science) , Robotics
- Description: Robotic simulators for use in Evolutionary Robotics (ER) have certain challenges associated with the complexity of their construction and the accuracy of predictions made by these simulators. Such robotic simulators are often based on physics models, which have been shown to produce accurate results. However, the construction of physics-based simulators can be complex and time-consuming. Alternative simulation schemes construct robotic simulators from empirically-collected data. Such empirical simulators, however, also have associated challenges, such as that some of these simulators do not generalize well on the data from which they are constructed, as these models employ simple interpolation on said data. As a result of the identified challenges in existing robotic simulators for use in ER, this project investigates the potential use of Artificial Neural Networks, henceforth simply referred to as Neural Networks (NNs), as alternative robotic simulators. In contrast to physics models, NN-based simulators can be constructed without needing an explicit mathematical model of the system being modeled, which can simplify simulator development. Furthermore, the generalization capabilities of NNs suggest that NNs could generalize well on data from which these simulators are constructed. These generalization abilities of NNs, along with NNs’ noise tolerance, suggest that NNs could be well-suited to application in robotics simulation. Investigating whether NNs can be effectively used as robotic simulators in ER is thus the endeavour of this work. Since not much research has been done in employing NNs as robotic simulators, many aspects of the experimental framework on which this dissertation reports needed to be carefully decided upon. Two robot morphologies were selected on which the NN simulators created in this work were based, namely a differentially steered robot and an inverted pendulum robot. Motion tracking and robotic sensor logging were used to acquire data from which the NN simulators were constructed. Furthermore, custom code was written for almost all aspects of the study, namely data acquisition for NN training, the actual NN training process, the evolution of robotic controllers using the created NN simulators, as well as the onboard robotic implementations of evolved controllers. Experimental tests performed in order to determine ideal topologies for each of the NN simulators developed in this study indicated that different NN topologies can lead to large differences in training accuracy. After performing these tests, the training accuracy of the created simulators was analyzed. This analysis showed that the NN simulators generally trained well and could generalize well on data not presented during simulator construction. In order to validate the feasibility of the created NN simulators in the ER process, these simulators were subsequently used to evolve controllers in simulation, similar to controllers developed in related studies. Encouraging results were obtained, with the newly-evolved controllers allowing real-world experimental robots to exhibit obstacle avoidance and light-approaching behaviour with a reasonable degree of success. The created NN simulators furthermore allowed for the successful evolution of a complex inverted pendulum stabilization controller in simulation. It was thus clearly established that NN-based robotic simulators can be successfully employed as alternative simulation schemes in the ER process.
- Full Text:
- Date Issued: 2010
- Authors: Pretorius, Christiaan Johannes
- Date: 2010
- Subjects: Neural networks (Computer science) , Robotics
- Language: English
- Type: Thesis , Masters , MSc
- Identifier: vital:10462 , http://hdl.handle.net/10948/1476 , Neural networks (Computer science) , Robotics
- Description: Robotic simulators for use in Evolutionary Robotics (ER) have certain challenges associated with the complexity of their construction and the accuracy of predictions made by these simulators. Such robotic simulators are often based on physics models, which have been shown to produce accurate results. However, the construction of physics-based simulators can be complex and time-consuming. Alternative simulation schemes construct robotic simulators from empirically-collected data. Such empirical simulators, however, also have associated challenges, such as that some of these simulators do not generalize well on the data from which they are constructed, as these models employ simple interpolation on said data. As a result of the identified challenges in existing robotic simulators for use in ER, this project investigates the potential use of Artificial Neural Networks, henceforth simply referred to as Neural Networks (NNs), as alternative robotic simulators. In contrast to physics models, NN-based simulators can be constructed without needing an explicit mathematical model of the system being modeled, which can simplify simulator development. Furthermore, the generalization capabilities of NNs suggest that NNs could generalize well on data from which these simulators are constructed. These generalization abilities of NNs, along with NNs’ noise tolerance, suggest that NNs could be well-suited to application in robotics simulation. Investigating whether NNs can be effectively used as robotic simulators in ER is thus the endeavour of this work. Since not much research has been done in employing NNs as robotic simulators, many aspects of the experimental framework on which this dissertation reports needed to be carefully decided upon. Two robot morphologies were selected on which the NN simulators created in this work were based, namely a differentially steered robot and an inverted pendulum robot. Motion tracking and robotic sensor logging were used to acquire data from which the NN simulators were constructed. Furthermore, custom code was written for almost all aspects of the study, namely data acquisition for NN training, the actual NN training process, the evolution of robotic controllers using the created NN simulators, as well as the onboard robotic implementations of evolved controllers. Experimental tests performed in order to determine ideal topologies for each of the NN simulators developed in this study indicated that different NN topologies can lead to large differences in training accuracy. After performing these tests, the training accuracy of the created simulators was analyzed. This analysis showed that the NN simulators generally trained well and could generalize well on data not presented during simulator construction. In order to validate the feasibility of the created NN simulators in the ER process, these simulators were subsequently used to evolve controllers in simulation, similar to controllers developed in related studies. Encouraging results were obtained, with the newly-evolved controllers allowing real-world experimental robots to exhibit obstacle avoidance and light-approaching behaviour with a reasonable degree of success. The created NN simulators furthermore allowed for the successful evolution of a complex inverted pendulum stabilization controller in simulation. It was thus clearly established that NN-based robotic simulators can be successfully employed as alternative simulation schemes in the ER process.
- Full Text:
- Date Issued: 2010
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