Automatically Addressing Uncertainty in Autonomous Robots with Computational Evolution
Advisor: Dr. Philip K. McKinley
Outstanding Graduate Student Service Award
Magna cum laude
Research: mobile robotics • deep learning
Teaching: data structures • algorithms • neural networks • computer systems
Visiting researcher working in Dr. Soon-Jo Chung’s ARC Lab
Tenure-track assistant professor.
Administered two lab sections of Introduction to Programming II
Organized and taught Introduction to Programming II (CSE232) during the summer session.
Addressed optimization, adaptive control, and fabrication of bio-inspired mobile robotic systems.
Designed software used to capture images at specified GPS locations with an autonomous aerial vehicle.
Investigated the fundamentals of convergence of complex solutions in power systems.
Solved problems associated with positioning error due to antenna performance.
Attended lectures covering the fundamentals of data science and worked on a team to create a reverse image search engine.
Attended lectures on the subject I was tutoring, provided walk-in, free tutoring consistent with course instruction, and led review sessions prior to exams.
Nominated by the Computer Science Department and selected by the college awards committee.
Completed the Advising Basics Workshop and the Master Advisor Workshop at Missouri State University. These workshops are day-long training sessions.
Completed training for recruiting and retaining low-income students from historically underrepresented groups including first generation students.
Matthew J. Rose, Anthony J. Clark, Jared M. Moore, and Philip K. McKinley. Just Keep Swimming: Accounting for Uncertainty in Self-Modeling Aquatic Robots. In Proceedings of the 6th International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems, Taormina, Italy, September 2013.
Anthony J. Clark, Jared Moore, Jianxun Wang, Xiaobo Tan, and Philip McKinley. Evolutionary design and experimental validation of a flexible caudal fin for robotic fish. In Proceedings of the Thirteenth International Conference on the Synthesis and Simulation of Living Systems, East Lansing, Michigan, USA, pages 325-332, July 2012.
Nominated by faculty at Michigan State University. This award was for $5,000 per year.
Nominated by the Computer Science Graduate Program at Michigan State University. This award guaranteed a research assistantship for six years.
Semantics From Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots
IEEE/RSJ International Conference on Intelligent Robots and Systems. (IROS 2024), Abu Dhabi, UAE.
@inproceedings{Lee.2024.IROS.SegmentationAnnotation,
abstract = "We present a new method to automatically generate semantic segmentation annotations for thermal imagery captured from an aerial vehicle by utilizing satellite-derived data products alongside onboard global positioning and attitude estimates. This new capability overcomes the challenge of developing thermal semantic perception algorithms for field robots due to the lack of annotated thermal field datasets and the time and costs of manual annotation, enabling precise and rapid annotation of thermal data from field collection efforts at a massively-parallelizable scale. By incorporating a thermal-conditioned refinement step with visual foundation models, our approach can produce highly-precise semantic segmentation labels using low-resolution satellite land cover data for little-to-no cost. It achieves 98.5% of the performance from using costly high-resolution options and demonstrates between 70-160% improvement over popular zero-shot semantic segmentation methods based on large vision-language models currently used for generating annotations for RGB imagery. Code will be available at: https://github.com/connorlee77/aerial-auto-segment.",
author = "Lee, Connor and Soedarmadji, Saraswati and Anderson, Matthew and Clark, Anthony J. and Chung, Soon-Jo",
location = "Abu Dhabi, UAE",
booktitle = "{IEEE/RSJ} International Conference on Intelligent Robots and Systems.",
date = "2024-10-14",
eventtitle = "{IROS} 2024",
title = "Semantics from Space: Satellite-Guided Thermal Semantic Segmentation Annotation for Aerial Field Robots",
}
Training and Deploying Deep Learning Models for Real-Time Pathfinding in Indoor Environments
Southern California Robotics Symposium (SCR 2024), Riverside, California, USA.
@inproceedings{Clark.2024.SCRS.Pathfinding,
abstract = "Pathfinding in dynamic, indoor environments is fundamental to the reliable, safe, and real-time navigation of autonomous systems. In this study, we present our research generating datasets and comparing deep learning architectures for real-time pathfinding in indoor environments. We use simulation for data collection, compare six architectures, and analyze real-time inference performance on an NVIDIA JetBot. Our work showcases an end-to-end pathfinding approach and highlights challenges to address in future research.",
author = "Atawya, Aser and Au, Kellie and Morales Puente, Francisco and Ryan, Tommy and Zhu, Ella and Clark, Anthony J.",
location = "Riverside, California, USA",
booktitle = "Southern California Robotics Symposium",
date = "2024-09-20",
eventtitle = "{SCR} 2024",
title = "Training and Deploying Deep Learning Models for Real-Time Pathfinding in Indoor Environments",
}
Development of a Hybrid Wheel/Leg Robot Using a Geared Coaxial Shaft Mechanism
Southern California Conference for Undergraduate Research (SCCUR 2023), Fullerton, California, USA.
@inproceedings{Clinton.2023.SCCUR.HybridWheelLeg,
abstract = " Wheeled locomotion is a power-efficient, generally safer, and easy to control form of robot locomotion. Compared with legged or flying robots, however, wheeled robots struggle with uneven terrain and simple, medium-sized obstacles (i.e., obstacles roughly the size of their wheels). Legged locomotion solves this challenge by stepping or climbing over obstacles, but suffers from power consumption, poor stability, and control complexity. Hybrid wheel/leg robots transform their locomotion systems between wheeled and legged modes to maximize the performance benefits of each locomotion mode. We present our design of a novel hybrid wheel/leg robot that uses a geared coaxial shaft mechanism to transform between states of wheeled and legged-wheel locomotion. The mechanism is also able to operate at intermediate states between these two modes. We present our mechanical design, development of a basic control policy, and preliminary experiments demonstrating basic capabilities of the robot. We show that the robot is able to navigate flat terrain in the wheeled locomotion mode, and can make use of hybrid legged locomotion modes to navigate rough terrain and obstacles. In the future, we plan to iterate on the mechanism's design, develop and evaluate more advanced control policies that leverage the mechanism's intermediate locomotion modes, and evaluate the mechanism's performance in real-world environments.",
author = "Clinton, James and Clark, Anthony J.",
location = "Fullerton, California, USA",
booktitle = "Southern California Conference for Undergraduate Research",
date = "2023-11-18",
eventtitle = "{SCCUR} 2023",
title = "Development of a Hybrid Wheel/Leg Robot Using a Geared Coaxial Shaft Mechanism",
}
Creating Dynamic Simulation Environments With Unreal Engine 5
Southern California Robotics Symposium (SCR 2023), Irvine, California, USA.
@inproceedings{MoralesPuente.2023.SCRS.UE5Simulation,
abstract = "Simulation is a vital component for many machine learning-based systems. In this abstract, we present our work using Unreal Engine 5 to create realistic environments for training a neural network used in the navigation system of a mobile robot. We explore the use of randomized textures to create dynamic environments, and we evaluate trained models in environments with both randomly changing and static textures.",
author = "Abbott, Daisy and Nuggehalli, Anjali and Morales Puente, Francisco and Vu, Chau and Zhu, Ella and Clark, Anthony J.",
location = "Irvine, California, USA",
booktitle = "Southern California Robotics Symposium",
date = "2023-09-22",
eventtitle = "{SCR} 2023",
title = "Creating Dynamic Simulation Environments With Unreal Engine 5",
}
Does Kinematic-Based Pretraining Improve Evolution of Quadrupedal Gaits?
Conference on Artificial Life (ALIFE 2023), Sapporo, Japan.
@inproceedings{Ayala.2023.ALIFE.Pretraining,
abstract = "Neural networks are often chosen as controllers in evolutionary robotics. In all but a few cases, neural networks are evolved from scratch. In this study, we investigate the effect of pretraining neural networks using a biologically inspired walking gait. We first generate joint angles for a walking gait using an inverse kinematics model. We then train a conventional feed-forward neural network to reproduce these joint angles. The pretrained model is used to seed an initial population of neural networks, which are coevolved along with the morphology of a quadrupedal robot using Lexicase selection. Our initial results show that while pretraining does not necessarily lead to higher fitness at the end of evolution, it does lead to more consistent performance and more lifelike final behaviors. This exploration has left us with many questions about the importance and process of pretraining in evolutionary robotics, and we believe our results suggest the technique is worth further investigation.",
author = "Ayala Ahumada, Kevin J. and Moore, Jared M. and Clark, Anthony J.",
location = "Sapporo, Japan",
publisher = "MIT Press",
booktitle = "Conference on Artificial Life",
date = "2023-07-24",
doi = "",
eventtitle = "{ALIFE} 2023",
title = "Does Kinematic-Based Pretraining Improve Evolution of Quadrupedal Gaits?",
}
Searching for Problematic Simulation Conditions
Southern California Robotics Symposium (SCR 2022), Los Angeles, California, USA.
@inproceedings{Johnson.2022.SCRS.Problematic,
abstract = "Many robot use-cases put a robot in close contact with people. These scenarios require the robot to make complex decisions that---above all else---must be safe. Often, sensor processing and decision making methods rely heavily on machine learning, and these techniques are only as useful as the training dataset. Current methods and datasets do not account for enough variation or extraordinary conditions. We propose using novelty search to discover scenarios causing a model to behave poorly.",
author = "Johnson, Elizabeth and Heck, Simon and Hernandez, Keneth Gonzalez and Clark, Anthony J.",
location = "Los Angeles, California, USA",
booktitle = "Southern California Robotics Symposium",
date = "2022-09-22",
eventtitle = "{SCR} 2022",
title = "Searching for Problematic Simulation Conditions",
}
Investigating Neural Network Architectures, Techniques, and Datasets for Autonomous Navigation in Simulation
IEEE Symposium Series on Computational Intelligence (SSCI 2021), Orlando, Florida, USA.
@inproceedings{Chang.2021.SSCI.Architectures,
abstract = "Neural networks (NNs) are becoming an increas- ingly important part of mobile robot control systems. Com- pared with traditional methods, NNs (and other data-driven techniques) produce comparable—if not better—results while requiring less engineering knowhow. Training NNs, however, still requires exploration of a significant number of architectural, optimization, and evaluation options. In this study, we build a simulation environment, generate three image datasets using distinct techniques, train 652 models (including replicates) using a variety of architectures and paradigms (e.g., classification, regression, etc.), and evaluate the navigation ability of the model in simulation. Our goal is to explore a large number of model possibilities so that we can select the most promising for future study with a physical device. Training datasets leading to the best performing models were those that included a significant amount of noise from seemingly inefficient actions. The most promising models explicitly incorporated “memory” wherein previous actions were included as an input in the next step. Such models performed as good or better than conventional convolutional NNs, recurrent NNs, and custom architectures including two camera frames. Although trained models perform well in an environment matching the distribution of the training dataset, they fail when the simulation environment is altered in a seemingly insignificant manner. In robotics research it is often taken for granted that a model with good validation characteristics will perform well on the underlying task, but the results presented here show that there can often be a loose relationship between validation metrics and performance.",
author = "Chang, Oliver and Marchese, Christiana and Mejia, Jared and Clark, Anthony J.",
location = "Orlando, Florida, USA",
publisher = "{IEEE}",
booktitle = "{IEEE} Symposium Series on Computational Intelligence",
date = "2021-12-01",
doi = "",
eventtitle = "{SSCI} 2021",
isbn = "",
title = "Investigating Neural Network Architectures, Techniques, and Datasets for Autonomous Navigation in Simulation",
}
Supervision and Evolution: Pretraining Neural Networks for Quadrupedal Locomotion
Conference on Artificial Life (ALIFE 2021), Online. DOI: 10.1162/isal_a_00363
@inproceedings{Moore.2021.ALIFE.Pretrain,
abstract = "Neural networks (NNs) are effective controllers for evolutionary robotics, imposing few limits on potential gaits. Morphology evolved with a controller enables brain and body to become tightly coupled. Typically, NN parameters (sometimes architectures) and animat bodies are randomly initialized at the start of evolution. In this paper, we pretrain NNs with supervised learning, bootstrapping NN outputs towards oscillating behaviors prior to evolution. We focus on quadrupedal gaits as they are well-studied in biology and several common gait patterns have been identified, named, and studied by the research community. We hypothesize that performance of evolved gaits will improve with pretraining compared to beginning evolution with randomly initialized NNs. Our results show that only some pretraining regimens outperform (in terms of distance traveled and viability) random initialization of NN parameters. Furthermore, some regimens introduce an initial bias that is difficult to overcome, resulting in better initial performance but worse performance in the long term.",
author = "Moore, Jared M. and Clark, Anthony J.",
location = "Online",
publisher = "MIT Press",
booktitle = "Conference on Artificial Life",
date = "2021-07-19",
doi = "10.1162/isal_a_00363",
eventtitle = "{ALIFE} 2021",
title = "Supervision and Evolution: Pretraining Neural Networks for Quadrupedal Locomotion",
}
MorphWorld: A State Transition Simulator
Conference on Artificial Life (ALIFE 2020), Montreal, CA (Remote Conference). DOI: 10.1162/isal_a_00253
@inproceedings{Shan.2020.ALIFE.MorphWorld,
abstract = "Digital simulation enables a wide variety of research and applications underlying the study of artificial life. In evolutionary robotics applications, the focus is often on maximizing performance of an animat for a specific task. Analyzing evolved behaviors can be challenging, however, given the complex coupling of morphology and brain. In this paper, we introduce a simulation environment built to investigate animats capable of smoothly transitioning between operating modes (e.g., from cautious to aggressive or from one physical form to another). The simulator provides functionality for logging sensory information as well as animat state enabling a deep analysis. Although more abstract than soft-body or rigid-body physics engines, it is lightweight and efficient, allowing for a high number of simulations in a small amount of time. The simulation supplements other more complex physics-based environments providing for greater inspection of sensor information and animat behavior. Furthermore, it is designed to provide an extensible test bed beyond just gait transitions to assess new artificial intelligence and evolutionary algorithms and more importantly the combination of these techniques.",
author = "Shan, Matthew and Moore, Jared M. and Clark, Anthony J.",
location = "Montreal, CA (Remote Conference)",
publisher = "MIT Press",
booktitle = "Conference on Artificial Life",
date = "2020-07-13",
doi = "10.1162/isal_a_00253",
eventtitle = "{ALIFE} 2020",
pages = "747--749",
title = "MorphWorld: A State Transition Simulator",
}
Comparing CNN Inputs for Terrain Classification Using Simulation
IEEE Transdisciplinary AI (TransAI 2019), Laguna Hills, California, USA. DOI: 10.1109/TransAI46475.2019.00015
@inproceedings{Clark.2019.TA.ComparingCNNInputs,
abstract = "Mobile robots frequently operate in rough, uneven terrain. One way for them to identify easier to traverse paths is to use deep learning methods, such as a convolutional neural network (CNN). It is not clear, however, what input should be provided to the CNN to best enable it to classify different types of terrain. In this study, we investigate and compare several different inputs formats for improving terrain classification using a CNN. All experiments take place in simulation, where we have complete control over terrain (e.g., shapes and textures) and information about our robot. Our experiments lead us to the following: (1) input formats should prefer grayscale over color images as color has a tendency to overfit the training data, and (2) disparity maps also improve classification compared with raw image data. These results can be used to improve the performance of terrain classification; particularly as they apply to transformable-wheel robots.",
author = "Clark, Anthony J. and Simpson, Jesse and Hall, Jared",
location = "Laguna Hills, California, USA",
booktitle = "{IEEE} Transdisciplinary {AI}",
date = "2019-09-25",
doi = "10.1109/TransAI46475.2019.00015",
eventtitle = "{TransAI} 2019",
isbn = "978-1-7281-4127-5",
pages = "43--47",
title = "Comparing {CNN} Inputs for Terrain Classification Using Simulation",
}
Construct of Sarcasm on Social Media Platform
IEEE International Conference on Humanized Computing and Communication (HCC 2019), Laguna Hills, California, USA. DOI: 10.1109/HCC46620.2019.00023
@inproceedings{Das.2019.HCC.ConstructSarcasmSocial,
abstract = "The basic idea behind machine learning-based systems, or artificial intelligence in general, is mimicking how humans operate. This idea is particularly true for our problem, sarcasm detection on social networking sites (SNSs). Therefore, before proceeding to build a system that can detect sarcasm on SNSs, we attempt to understand how humans do the same. Many studies propose approaches based on personal experience and word-level definition of {"}sarcasm{"}. However, in this paper, we aim to find more general themes that are typical with users while detecting and expressing sarcasm on SNSs through a qualitative study to build a more effective sarcasm detection model.",
author = "Das, Dipto and Clark, Anthony J.",
location = "Laguna Hills, California, USA",
booktitle = "{IEEE} International Conference on Humanized Computing and Communication",
date = "2019-09-25",
doi = "10.1109/HCC46620.2019.00023",
eventtitle = "{HCC} 2019",
isbn = "978-1-7281-4125-1",
pages = "106--113",
title = "Construct of Sarcasm on Social Media Platform",
}
Satire vs Fake News: You Can Tell by the Way They Say It
IEEE Transdisciplinary AI (TransAI 2019), Laguna Hills, California, USA. DOI: 10.1109/TransAI46475.2019.00012
@inproceedings{Das.2019.TA.SatireVsFake,
abstract = "In recent times, {"}fake news{"} has become an increasingly important concept. Primarily, because information is now able to more quickly and deeply propagate among users due to the pervasive nature of the Internet and digital media. That is why it has recently received a large amount of attention from computer science researchers. A large number of studies demonstrate different methods for detecting misinformation in contents shared on the Internet. On the other hand, satire and irony as a part of usual human communication have received less attention. Whereas fake news means misinformation meant to deceive people, satire is misinformation meant to entertain or criticize. Thus, despite both satire and fake news being misinformation these two concepts have different objectives and impacts. Currently, only a few studies have focused on differentiating between satire and fake news. In this paper, we present the limitations of existing works for classifying satire and fake news; discuss the feasibility of using a subjective concept like storytelling as a way to classify satire and fake news; and present a supervised learning approach to classify satire and fake news.",
author = "Das, Dipto and Clark, Anthony J.",
location = "Laguna Hills, California, USA",
booktitle = "{IEEE} Transdisciplinary {AI}",
date = "2019-09-25",
doi = "10.1109/TransAI46475.2019.00012",
eventtitle = "{TransAI} 2019",
isbn = "978-1-7281-4127-5",
pages = "22--26",
title = "Satire vs Fake News: You Can Tell by the Way They Say It",
}
Understanding the Attention Model of Humans in Sarcastic Videos
IEEE Transdisciplinary AI (TransAI 2019), Laguna Hills, California, USA. DOI: 10.1109/TransAI46475.2019.00022
@inproceedings{Das.2019.TA.UnderstandingAttentionModel,
abstract = "Sarcasm is a usual part of human communication that has long been ignored by sentiment analysis researchers. Sarcasm is also an important aspect in entertainment industry for TV series, movies etc. to gain popularity. Very recently, some works have showed the applicability of multimodality (e.g., image, text) in sarcasm research from a sentiment analysis perspective instead of only text-based approaches. However, none of those harnesses video data. We argue videos can be interesting to study to understand nature of sarcasm on social media. We are interested to study how sarcastic videos gain individual's attention and popularity at large. As an application of this, we showed how an AI agent can suggest about possible areas to gain viewers' attention in a directed sarcastic video. Identification of both attention gaining areas (AGA) and objects contained in sarcastic videos can be compared with the AGAs and objects in previously successful/popular sarcastic videos. Such AI agent can help inexperienced directors in entertainment industry as a guide and experienced ones to study the changes brought over time in this regard. In this paper, we present two AI agents to identify the optimal AGAs and one empirical study of objects commonly shown in directed sarcastic video settings.",
author = "Das, Dipto and Hossain, Md Forhad and Clark, Anthony J.",
location = "Laguna Hills, California, USA",
booktitle = "{IEEE} Transdisciplinary {AI}",
date = "2019-09-25",
doi = "10.1109/TransAI46475.2019.00022",
eventtitle = "{TransAI} 2019",
isbn = "978-1-7281-4127-5",
pages = "84--87",
title = "Understanding the Attention Model of Humans in Sarcastic Videos",
}
Improve Quadrupedal Locomotion With Actuated or Passive Joints?
Conference on Artificial Life (ALIFE 2019), Newcastle, United Kingdom. DOI: 10.1162/isal_a_00221
@inproceedings{Moore.2019.ALIFE.ImproveQuadrupedalLocomotion,
abstract = "Animals interact with their environment softly through interaction of muscles, tendons, and rigid skeleton. By incorporating flexibility, they reduce ground impact forces and improve locomotive efficiency. Flexibility is also beneficial for robotic systems, although it remains challenging to implement. In this paper, we explore the addition of passive flexibility to a quadrupedal animat; we measure the impact of flexibility on both locomotive performance and energy efficiency of movement. Results show that spine and lower limb flexibility can significantly increase distance traveled when compared to an animat with no flexibility. However, replacing passively flexibile joints with actively controlled joints evolves more effective individuals with similar efficiency. Given these results, the number of joints and joint configuration appear to drive performance increases rather than just the addition of passive flexibility.",
author = "Moore, Jared M. and Clark, Anthony J.",
location = "Newcastle, United Kingdom",
publisher = "MIT Press",
booktitle = "Conference on Artificial Life",
date = "2019-07-29",
doi = "10.1162/isal_a_00221",
eventtitle = "{ALIFE} 2019",
isbn = "978-0-262-35844-6",
pages = "559--566",
title = "Improve Quadrupedal Locomotion with Actuated or Passive Joints?",
}
Evolving Controllers for a Transformable Wheel Mobile Robot
Complexity. DOI: 10.1155/2018/7692042
@article{Clark.2018.Complexity.EvolvingControllersTransformable,
abstract = "Unmanned ground vehicles (UGVs) are well suited to tasks that are either too dangerous or too monotonous for people. For example, UGVs can traverse arduous terrain in search of disaster victims. However, it is difficult to design these systems so that they perform well in a variety of different environments. In this study, we evolve controllers and physical characteristics of a UGV with transformable wheels to improve its mobility in a simulated environment. The UGV’s mission is to visit a sequence of coordinates while automatically handling obstacles of varying sizes by extending wheel struts radially outward from the center of each wheel. Evolved finite state machines (FSMs) and artificial neural networks (ANNs) are compared, and a set of controller design principles are gathered from analyzing these experiments. Results show similar performance between FSM and ANN controllers but differing strategies. Finally, we show that a UGV’s controller and physical characteristics can be effectively chosen by examining results from evolutionary optimization.",
author = "Clark, Anthony J. and Cissell, Keith A. and Moore, Jared M.",
date = "2018-12-16",
doi = "10.1155/2018/7692042",
issn = "1076-2787, 1099-0526",
journaltitle = "Complexity",
langid = "english",
title = "Evolving Controllers for a Transformable Wheel Mobile Robot",
volume = "2018",
}
An Ensemble of Face Recognition Algorithms for Unsupervised Expansion of Training Data
International Conference on Computational Science and Computational Intelligence (CSCI 2018), Las Vegas, Nevada, USA. DOI: 10.1109/CSCI.2018.00072
@inproceedings{Dale.2018.CSCI.EnsembleFaceRecognition,
abstract = "Facial recognition is a classical problem in computer vision. The accuracy of face recognition algorithms is crucial in practice, as systems are increasingly secured with biometric locks. However, the performance of these algorithms is heavily dependent upon the size of the training data. This paper proposes an unsupervised ensemble method for expanding the set of training faces when only a single labeled face per subject is known. We show that the ensemble’s confidence measure is sufficient to expand the training set to the point where more sophisticated algorithms can take over in classification.",
author = "Dale, Jeffrey and Clark, Anthony J.",
location = "Las Vegas, Nevada, USA",
booktitle = "International Conference on Computational Science and Computational Intelligence",
date = "2018-12-13",
doi = "10.1109/CSCI.2018.00072",
eventtitle = "{CSCI} 2018",
isbn = "978-1-72811-360-9",
langid = "english",
title = "An Ensemble of Face Recognition Algorithms for Unsupervised Expansion of Training Data",
}
Sarcasm Detection on Facebook: A Supervised Learning Approach
International Conference on Multimodal Interaction Adjunct (ICMI 2018), Boulder, Colorado, USA. DOI: 10.1145/3281151.3281154
@inproceedings{Das.2018.ICMIA.SarcasmDetectionFacebook,
abstract = "Sarcasm is a common feature of user interaction on social networking sites. Sarcasm differs with typical communication in alignment of literal meaning with intended meaning. Humans can recognize sarcasm from sufficient context information including from the various contents available on SNS. Existing literature mainly uses text data to detect sarcasm; though, a few recent studies propose to use image data. To date, no study has focused on user interaction pattern as a source of context information for detecting sarcasm. In this paper, we present a supervised machine learning based approach focusing on both contents of posts (e.g., text, image) and users' interaction on those posts on Facebook.",
author = "Das, Dipto and Clark, Anthony J.",
location = "Boulder, Colorado, USA",
publisher = "ACM Press",
booktitle = "International Conference on Multimodal Interaction Adjunct",
date = "2018-10-16",
doi = "10.1145/3281151.3281154",
eventtitle = "{ICMI} 2018",
isbn = "978-1-4503-6002-9",
shorttitle = "Sarcasm Detection on {Facebook}",
title = "Sarcasm Detection on {Facebook}: A Supervised Learning Approach",
}
Sarcasm Detection on Flickr Using a CNN
International Conference on Computing and Big Data (ICCBD 2018), Charleston, South Carolina, USA. DOI: 10.1145/3277104.3277118
@inproceedings{Das.2018.ICCBD.SarcasmDetectionFlickr,
abstract = "Sarcasm is an important aspect of human communication. However, it is often difficult to detect or understand this sentiment because the literal meaning conveyed in communication is opposite of the intended meaning. Though the field of sentiment analysis is well studied, sarcasm has often been ignored by the research community. So far, to detect sarcasm on social media, studies have largely focused upon textual features. However, visual cues are an important part of sarcasm. In this paper, we present a convolutional neural network based model for detecting sarcasm based on images shared on a popular social photo sharing site, Flickr.",
author = "Das, Dipto and Clark, Anthony J.",
location = "Charleston, South Carolina, USA",
publisher = "ACM Press",
booktitle = "International Conference on Computing and Big Data",
date = "2018-09-08",
doi = "10.1145/3277104.3277118",
eventtitle = "{ICCBD} 2018",
isbn = "978-1-4503-6540-6",
pages = "56--61",
title = "Sarcasm Detection on {F}lickr Using a {CNN}",
}
Review: A Web-Based Simulation Viewer for Sharing Evolutionary Robotics Results
Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan. DOI: 10.1145/3205651.3208292
@inproceedings{Clark.2018.GECCO.ReviewWebbasedSimulation,
abstract = "Evolutionary robotics researchers often need to share results that may be too difficult to describe in text and too complex to show using images. Many researchers include links to videos as supplementary materials, but videos have a predefined view of the scene and do not allow watchers to adjust the viewing angle to their preference. In this paper we present a web-based application (based on three.js) for sharing interactive animations. Specifically, our tool (called Review) enables researchers to generate simple animation log data that can be loaded in any modern web browser on a computer or mobile device. The camera in these animations is controlled by the user such that they can pan, tilt, rotate, and zoom in and out of the scene. Review is meant to improve the ability of researchers to share their evolved results with one another.",
author = "Clark, Anthony J. and Moore, Jared M.",
location = "Kyoto, Japan",
publisher = "ACM Press",
booktitle = "Genetic and Evolutionary Computation Conference",
date = "2018-07-15",
doi = "10.1145/3205651.3208292",
eventtitle = "{GECCO} 2018",
isbn = "978-1-4503-5764-7",
pages = "1357--1362",
shorttitle = "Review",
title = "Review: A Web-Based Simulation Viewer for Sharing Evolutionary Robotics Results",
}
Bend and Flex: Passive Flexibility or Active Control in a Quadruped Animat
Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan. DOI: 10.1145/3205651.3205703
@inproceedings{Moore.2018.GECCO.BendFlexPassive,
abstract = "Muscle and tendon elasticity enables animals to interact with their environment softly, reducing ground impact force and increasing efficiency of locomotion. Traditional rigid body robots remain the commercially viable option, but incorporating flexibility can harness the benefits exhibited by natural organisms. In this paper, we examine how the addition of passive flexibility impacts performance and locomotive efficiency in a quadruped animat. Results show that the addition of flexibility in the spine and lower limbs of a quadruped animat significantly increases the distance traveled compared to a fully rigid-body animat. However, replacing these passively flexibile joints with actively controlled joints results in the farthest traveling individuals while maintaining similar efficiency. It appears that increases in DOF and joint configuration are the drivers of performance increases rather than passive flexibility.",
author = "Moore, Jared M. and Clark, Anthony J.",
location = "Kyoto, Japan",
publisher = "ACM Press",
booktitle = "Genetic and Evolutionary Computation Conference",
date = "2018-07-15",
doi = "10.1145/3205651.3205703",
eventtitle = "{GECCO} 2018",
isbn = "978-1-4503-5764-7",
shorttitle = "Bend and Flex",
title = "Bend and Flex: Passive Flexibility or Active Control in a Quadruped Animat",
}
Evo-ROS: Integrating Evolution and the Robot Operating System
Genetic and Evolutionary Computation Conference (GECCO 2018), Kyoto, Japan. DOI: 10.1145/3205651.3208269
@inproceedings{Simon.2018.GECCO.EvoROSIntegratingEvolution,
abstract = "In this paper, we describe the Evo-ROS framework, which is intended to help bridge the gap between the evolutionary and traditional robotics communities. Evo-ROS combines an evolutionary algorithm with individual physics-based evaluations conducted using the Robot Operating System (ROS) and the Gazebo simulation environment. Our goals in developing Evo-ROS are to (1) provide researchers in evolutionary robotics with access to the extensive support for real-world components and capabilities developed by the ROS community and (2) enable ROS developers, and more broadly robotics researchers, to take advantage of evolutionary search during design and testing. We describe the details of the Evo-ROS structure and operation, followed by presentation of a case study using Evo-ROS to optimize placement of sonar sensors on unmanned ground vehicles that can experience reduced sensing capability due to component failures and physical damage. The case study provides insights into the current capabilities and identifies areas for future enhancements.",
author = "Simon, Glen A. and Moore, Jared M. and Clark, Anthony J. and McKinley, Philip K.",
location = "Kyoto, Japan",
publisher = "ACM Press",
booktitle = "Genetic and Evolutionary Computation Conference",
date = "2018-07-15",
doi = "10.1145/3205651.3208269",
eventtitle = "{GECCO} 2018",
isbn = "978-1-4503-5764-7",
pages = "1386--1393",
shorttitle = "Evo-{ROS}",
title = "Evo-{ROS}: Integrating Evolution and the Robot Operating System",
}
Evolving Adabot: A Mobile Robot With Adjustable Wheel Extensions
IEEE Symposium Series on Computational Intelligence (RiiSS 2017), Honolulu, Hawaii, USA. DOI: 10.1109/SSCI.2017.8280979
@inproceedings{Clark.2017.SSCI.EvolvingAdabotMobile,
abstract = "Robots are increasingly being utilized in unstructured environments. Autonomous mobile robots are being assigned with tasks that are either too difficult or too dangerous for people. For instance, search and rescue robots can be deployed in unstable environments to aid in the search for disaster victims. In this paper, we propose a novel design for an autonomous mobile robot that can dynamically adjust traction during runtime. Our device, called Adabot meaning adaptive robot, is small, has a simple design, and can extend wegs from its wheels by adjustable amounts. We optimize both the morphology and the control parameters of Adabot using differential evolution, and our simulation results show that Adabot is effectively able to take advantage of both purely wheeled locomotion and legged-wheel locomotion by transitioning automatically between these two modes.",
author = "Clark, Anthony J.",
location = "Honolulu, Hawaii, USA",
publisher = "{IEEE}",
booktitle = "{IEEE} Symposium Series on Computational Intelligence",
date = "2017-12-01",
doi = "10.1109/SSCI.2017.8280979",
eventtitle = "{RiiSS} 2017",
isbn = "978-1-5386-2726-6",
shorttitle = "Evolving Adabot",
title = "Evolving Adabot: A Mobile Robot with Adjustable Wheel Extensions",
}
Effect of Animat Complexity on the Evolution of Hierarchical Control
Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany. DOI: 10.1145/3071178.3071246
@inproceedings{Moore.2017.GECCO.EffectAnimatComplexity,
abstract = "Animal movements are realized by a combination of high-level control from the nervous system and joint-level movement provided by the musculoskeletal system. The digital muscle model (DMM) emulates the low-level musculoskeletal system and can be combined with a high-level artificial neural network (ANN) controller forming a hybrid control strategy. Previous work has shown that, compared to ANN-only controllers, hybrid ANN/DMM controllers exhibit similar performance with fewer synapses, suggesting that some computation is offloaded to the low-level DMM. An open question is how the complexity of the robot, in terms of the number of joints, affects the evolution of the ANN control structure. We explore this question by evolving both hybrid controllers and ANN-only controllers for worm-like animats of varying complexity. Specifically, the number of joints in the worms ranges from 1 to 12. Consistent with an earlier study, the results demonstrate that, in most cases, hybrid ANN/DMM controllers exhibit equal or better performance than ANN-only controllers. In addition, above a threshold for animat complexity (number of joints), the ANNs for one variant of the hybrid controllers have significantly fewer connections than the ANN-only controllers.",
author = "Moore, Jared M. and Clark, Anthony J. and McKinley, Philip K.",
location = "Berlin, Germany",
publisher = "ACM Press",
booktitle = "Genetic and Evolutionary Computation Conference",
date = "2017-07-15",
doi = "10.1145/3071178.3071246",
eventtitle = "{GECCO} 2017",
isbn = "978-1-4503-4920-8",
pages = "147--154",
title = "Effect of Animat Complexity on the Evolution of Hierarchical Control",
}
An Evolutionary Approach to Discovering Execution Mode Boundaries for Adaptive Controllers
IEEE Symposium Series on Computational Intelligence (SSCI 2016), Athens, Greece. DOI: 10.1109/SSCI.2016.7850178
@inproceedings{Clark.2016.ICES.EvolutionaryApproachDiscovering,
abstract = "Adaptive controllers enable cyberphysical systems, such as autonomous robots, to manage uncertain conditions during execution. However, there is a limit to the range of conditions that can be handled by a given controller. When this limit is exceeded, a controller might fail to respond as expected, not only rendering it ineffective but possibly putting the entire system at risk. In this paper, we describe a method based on evolutionary search for automatically enhancing, and discovering the boundaries of, a given adaptive controller. Collectively, these boundaries define an execution mode for that controller. Explicit specification of mode boundaries facilitates the development of decision logic that determines, based on system state and sensed conditions, when to switch to a different execution mode and typically a different controller, such as one for providing fail-safe operation. To evaluate the proposed approach, we apply it to a robotic fish propelled by a flexible caudal fin that is governed by a model-free adaptive controller. Experimental results demonstrate that this approach is effective in characterizing a controller’s ability to adapt to environmental dynamics, including physical damage to the robot itself.",
author = "Clark, Anthony J. and DeVries, Byron and Moore, Jared M. and Cheng, Betty H. C. and McKinley, Philip K.",
location = "Athens, Greece",
publisher = "{IEEE}",
booktitle = "{IEEE} Symposium Series on Computational Intelligence",
date = "2016-12-15",
doi = "10.1109/SSCI.2016.7850178",
eventtitle = "{SSCI} 2016",
isbn = "978-1-5090-4240-1",
title = "An Evolutionary Approach to Discovering Execution Mode Boundaries for Adaptive Controllers",
}
Evolutionary Multiobjective Design of a Flexible Caudal Fin for Robotic Fish
Bioinspiration & Biomimetics. DOI: 10.1088/1748-3190/10/6/065006
@article{Clark.2015.BB.EvolutionaryMultiobjectiveDesign,
abstract = "Robotic fish accomplish swimming by deforming their bodies or other fin-like appendages. As an emerging class of embedded computing system, robotic fish are anticipated to play an important role in environmental monitoring, inspection of underwater structures, tracking of hazardous wastes and oil spills, and the study of live fish behaviors. While integration of flexible materials (into the fins and/or body) holds the promise of improved swimming performance (in terms of both speed and maneuverability) for these robots, such components also introduce significant design challenges due to the complex material mechanics and hydrodynamic interactions. The problem is further exacerbated by the need for the robots to meet multiple objectives (e.g., both speed and energy efficiency). In this paper, we propose an evolutionary multiobjective optimization approach to the design and control of a robotic fish with a flexible caudal fin. Specifically, we use the NSGA-II algorithm to investigate morphological and control parameter values that optimize swimming speed and power usage. Several evolved fin designs are validated experimentally with a small robotic fish, where fins of different stiffness values and sizes are printed with a multi-material 3D printer. Experimental results confirm the effectiveness of the proposed design approach in balancing the two competing objectives.",
author = "Clark, Anthony J. and Tan, Xiaobo and McKinley, Philip K.",
date = "2015-11-25",
doi = "10.1088/1748-3190/10/6/065006",
issn = "1748-3190",
journaltitle = "Bioinspiration \& Biomimetics",
number = "6",
title = "Evolutionary Multiobjective Design of a Flexible Caudal Fin for Robotic Fish",
volume = "10",
}
Enhancing a Model-Free Adaptive Controller Through Evolutionary Computation
Genetic and Evolutionary Computation Conference (GECCO 2015), Madrid, Spain. DOI: 10.1145/2739480.2754762
@inproceedings{Clark.2015.GECCO.EnhancingModelFreeAdaptive,
abstract = "Many robotic systems experience fluctuating dynamics during their lifetime. Variations can be attributed in part to material degradation and decay of mechanical hardware. One approach to mitigating these problems is to utilize an adaptive controller. For example, in model-free adaptive control (MFAC) a controller learns how to drive a system by continually updating link weights of an artificial neural network (ANN). However, determining the optimal control parameters for MFAC, including the structure of the underlying ANN, is a challenging process. In this paper we investigate how to enhance the online adaptability of MFAC-based systems through computational evolution. We apply the proposed methods to a simulated robotic fish propelled by a flexible caudal fin. Results demonstrate that the robot is able to effectively respond to changing fin characteristics and varying control signals when using an evolved MFAC controller. Notably, the system is able to adapt to characteristics not encountered during evolution. The proposed technique is general and can be applied to improve the adaptability of other cyber-physical systems.",
author = "Clark, Anthony J. and McKinley, Philip K. and Tan, Xiaobo",
location = "Madrid, Spain",
publisher = "ACM Press",
booktitle = "Genetic and Evolutionary Computation Conference",
date = "2015-07-12",
doi = "10.1145/2739480.2754762",
eventtitle = "{GECCO} 2015",
isbn = "978-1-4503-3472-3",
langid = "english",
pages = "137--144",
title = "Enhancing a Model-Free Adaptive Controller through Evolutionary Computation",
}
Balancing Performance and Efficiency in a Robotic Fish With Evolutionary Multiobjective Optimization
IEEE International Conference on Evolvable Systems (ICES 2014), Orlando, Florida, USA. DOI: 10.1109/ICES.2014.7008744
@inproceedings{Clark.2014.ICES.BalancingPerformanceEfficiency,
abstract = "In this paper, we apply evolutionary multiobjective optimization to the design of a robotic fish with a flexible caudal fin. Specifically, we use the NSGA-II algorithm to discover solutions (physical dimensions, flexibility, and control parameters) that optimize both swimming performance and power efficiency. The optimization is conducted in a custom simulation environment based on an accurate yet computationally-efficient model of hydrodynamics. The results of these simulations reveal general principles that can be applied in the design of robotic fish morphology and control. To verify that the simulation results are physically relevant, we selected several of the evolved solutions, fabricated flexible caudal fins using a multi-material 3D printer, and attached them to a robotic fish prototype. Experimental results, conducted in a large water tank, correspond reasonably well to simulation results in both swimming performance and power efficiency, demonstrating the usefulness of evolutionary computation methods to this application domain.",
author = "Clark, Anthony J. and Wang, Jianxun and Tan, Xiaobo and McKinley, Philip K.",
location = "Orlando, Florida, USA",
publisher = "{IEEE}",
booktitle = "{IEEE} International Conference on Evolvable Systems",
date = "2014-12-15",
doi = "10.1109/ICES.2014.7008744",
eventtitle = "{ICES} 2014",
isbn = "978-1-4799-4479-8",
pages = "227--234",
title = "Balancing Performance and Efficiency in a Robotic Fish with Evolutionary Multiobjective Optimization",
}
On-Board Evolution of a Model-Free Adaptive Controller for a Robotic Fish
Evolution of Physical Systems Workshop, Held in Conjunction With the International Conference on the Synthesis and Simulation of Living Systems (ALIFE 2014), New York City, New York, USA.
@inproceedings{Clark.2014.ALIFE.OnBoardEvolutionModelFree,
abstract = "Many physical systems experience fluctuating dynamics throughout their lifetime. Variations can be attributed in part to material degradation and decay of mechanical hardware. Designing control strategies that mitigate the negative effects of such variations can be difficult. One approach is to utilize model-free adaptive control (MFAC), which learns how to control a system by continually updating link weights of an artificial neural network (ANN) (Cheng, 2004). However, determining the optimal values of various control parameters, as well as the structure of the ANN, is challenging. In this study, we investigate how to enhance the on-board adaptability of MFAC-based systems through computational evolution.",
author = "Clark, Anthony J. and McKinley, Philip K. and Tan, Xiaobo",
location = "New York City, New York, USA",
booktitle = "Evolution of Physical Systems Workshop, Held in Conjunction with the International Conference on the Synthesis and Simulation of Living Systems",
date = "2014-07-30",
eventtitle = "{ALIFE} 2014",
title = "On-Board Evolution of a Model-Free Adaptive Controller for a Robotic Fish",
}
Evolutionary Robotics on the Web With WebGL and JavaScript
International Conference on the Synthesis and Simulation of Living Systems (ALIFE 2014), New York City, New York, USA.
@inproceedings{Moore.2014.ALIFE.EvolutionaryRoboticsWeb,
abstract = "Web-based applications are highly accessible to users, providing rich, interactive content while eliminating the need to install software locally. However, evolutionary robotics (ER) has faced challenges in this domain as web-based technologies have not been amenable to 3D physics simulations. Traditionally, physics-based simulations require a local installation and a high degree of user knowledge to configure an environment, but the emergence of Javascript-based physics engines enables complex simulations to be executed in web browsers. These developments create opportunities for ER research to reach new audiences by increasing accessibility. In this work, we introduce two web-based tools we have built to facilitate the exchange of ideas with other researchers as well as outreach to K-12 students and the general public. The first tool is intended to distribute and exchange ER research results, while the second is a completely browser-based implementation of an ER environment.",
author = "Moore, Jared M. and Clark, Anthony J. and McKinley, Philip K.",
location = "New York City, New York, USA",
url = "http://arxiv.org/abs/1406.3337",
booktitle = "International Conference on the Synthesis and Simulation of Living Systems",
date = "2014-07-30",
eprint = "1406.3337",
eprinttype = "arxiv",
eventtitle = "{ALIFE} 2014",
title = "Evolutionary Robotics on the Web with {WebGL} and {JavaScript}",
}
Hold the Spot: Evolution of Generalized Station Keeping for an Aquatic Robot
International Conference on the Synthesis and Simulation of Living Systems (ALIFE 2014), New York City, New York, USA. DOI: 10.7551/978-0-262-32621-6-ch033
@inproceedings{Moore.2014.ALIFE.HoldSpotEvolution,
abstract = "In this paper, we present a strategy to evolve neurocontrollers in aquatic robots capable of generalized station keeping, that is, maintaining a position in the presence of various water flows. Evolved behaviors exhibit a variety of complex fin/flipper movements that enable the robot to react and move against changing flows. Moreover, results indicate that some sensor modalities are beneficial when the robot is placed in novel environments, though little used during the evolutionary process.",
author = "Moore, Jared M. and Clark, Anthony J.",
location = "New York City, New York, USA",
publisher = "The MIT Press",
booktitle = "International Conference on the Synthesis and Simulation of Living Systems",
date = "2014-07-30",
doi = "10.7551/978-0-262-32621-6-ch033",
eventtitle = "{ALIFE} 2014",
isbn = "978-0-262-32621-6",
shorttitle = "Hold the Spot",
title = "Hold the Spot: Evolution of Generalized Station Keeping for an Aquatic Robot",
}
Just Keep Swimming: Accounting for Uncertainty in Self-Modeling Aquatic Robots
International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems (ERLARS 2013), Taormina, Italy. Best Paper Award
@inproceedings{Rose.2013.ERLARS.JustKeepSwimming,
abstract = "A robust robotic system should be able to overcome unforeseen conditions, including physical damage and component failure occurring after deployment. A self-modeling system maintains an internal image of itself, which can be updated to reflect incurred damage. The robot can use this model to derive (or evolve) new behaviors such as gaits that account for the damage. In this paper we describe an approach to self-modeling for aquatic robots. The aquatic environment presents unique challenges to the self-modeling process, including the inherent uncertainty in the robot’s orientation and configuration. We propose and evaluate two approaches to automatically infer missing contextual information, which otherwise complicates the task of developing an accurate model. We demonstrate the effectiveness of these methods on a particular aquatic robot intended for remote sensing.",
author = "Rose, Matthew J. and Clark, Anthony J. and Moore, Jared M. and McKinley, Philip K.",
location = "Taormina, Italy",
booktitle = "International Workshop on Evolutionary and Reinforcement Learning for Autonomous Robot Systems",
date = "2013-09-15",
eventtitle = "{ERLARS} 2013",
shorttitle = "Just Keep Swimming",
title = "Just Keep Swimming: Accounting for Uncertainty in Self-Modeling Aquatic Robots",
note = "Best Paper Award",
}
Evolutionary Optimization of Robotic Fish Control and Morphology
Genetic and Evolutionary Computation Conference (GECCO 2013), Amsterdam, The Netherlands. DOI: 10.1145/2464576.2464593
@inproceedings{Clark.2013.GECCO.EvolutionaryOptimizationRobotic,
abstract = "The nonlinear dynamics of an aquatic environment make robotic fish behavior difficult to predict and subsequently difficult to optimize. In this paper, we present a method for optimizing robotic fish propulsion through the evolution of control patterns and caudal fin flexibility. Evolved solutions are evaluated in a physics-based simulation environment. Control signals are generated with both simple sinusoids and neural oscillators. This study demonstrates how evolutionary algorithms can be utilized to handle the complex interactions among material properties, physical form, and control patterns in an aquatic environment.",
author = "Clark, Anthony J. and McKinley, Philip K.",
location = "Amsterdam, The Netherlands",
publisher = "ACM Press",
booktitle = "Genetic and Evolutionary Computation Conference",
date = "2013-07-15",
doi = "10.1145/2464576.2464593",
eventtitle = "{GECCO} 2013",
isbn = "978-1-4503-1964-5",
langid = "english",
title = "Evolutionary Optimization of Robotic Fish Control and Morphology",
}
Evolution of Station Keeping as a Response to Flows in an Aquatic Robot
Genetic and Evolutionary Computation Conference (GECCO 2013), Amsterdam, The Netherlands. DOI: 10.1145/2463372.2463402
@inproceedings{Moore.2013.GECCO.EvolutionStationKeeping,
abstract = "Developing complex behaviors for aquatic robots is a difficult engineering challenge due to the uncertainty of an underwater environment. Neuroevolution provides one method of dealing with this type of problem. Artificial neural networks discern different conditions by mapping sensory input to responses, and evolutionary computation provides a training algorithm suitable to the high dimensionality of the problem. In this paper, we present results of applying neuroevolution to an aquatic robot tasked with station keeping, that is, maintaining a given position despite surrounding water flow. The virtual device exposed to evolution is modeled after a physical counterpart that has been fabricated with a 3D printer and tested in physical environments. Evolved behaviors exhibit a variety of unexpected, complex fin/flipper movements that enable the robot to achieve and maintain station, despite water flow from different directions. Moreover, the results show that evolved controllers are able to effectively carry out this task using only information from a simulated accelerometer and gyroscope, matching the inertial measurement unit (IMU) on the actual robot.",
author = "Moore, Jared M. and Clark, Anthony J. and McKinley, Philip K.",
location = "Amsterdam, The Netherlands",
publisher = "ACM Press",
booktitle = "Genetic and Evolutionary Computation Conference",
date = "2013-07-15",
doi = "10.1145/2463372.2463402",
eventtitle = "{GECCO} 2013",
isbn = "978-1-4503-1963-8",
langid = "english",
title = "Evolution of Station Keeping as a Response to Flows in an Aquatic Robot",
}
Evolutionary Design and Experimental Validation of a Flexible Caudal Fin for Robotic Fish
International Conference on the Synthesis and Simulation of Living Systems (ALIFE 2013), East Lansing, Michigan, USA. Best Paper Award DOI: 10.7551/978-0-262-31050-5-ch043
@inproceedings{Clark.2012.ALIFE.EvolutionaryDesignExperimental,
abstract = "Designing a robotic fish is a challenging endeavor due to the non-linear dynamics of underwater environments. In this paper, we present an evolutionary computation approach for designing the caudal fin of a carangiform robotic fish. Evolutionary experiments are performed in a simulated environment utilizing a mathematical model to approximate the hydrodynamic motion of a flexible caudal fin. With this model, time-consuming computational fluid dynamic simulations can be avoided while maintaining a physically realistic simulation. Two approaches are employed to maximize a robotic fish’s average velocity. First, a hill-climbing algorithm is applied to find the optimal stiffness for a fixed shape caudal fin. Next, both fin stiffness and shape are simultaneously optimized with a genetic algorithm. Additionally, simulated caudal fins are compared to physically validated fins, which were fabricated with the aid of a 3D printer and tested on a robotic fish prototype. Results show a correlation between evolved results, model predicted behavior, and physical robot performance with some disparity due to the difficulty in accurately approximating real world performance in a simulation environment. Despite the disparity, evolutionary design is shown to be a viable process.",
author = "Clark, Anthony J. and Moore, Jared M. and Wang, Jianxun and Tan, Xiaobo and McKinley, Philip K.",
location = "East Lansing, Michigan, USA",
publisher = "MIT Press",
booktitle = "International Conference on the Synthesis and Simulation of Living Systems",
date = "2012-07-02",
doi = "10.7551/978-0-262-31050-5-ch043",
eventtitle = "{ALIFE} 2013",
isbn = "978-0-262-31050-5",
pages = "325--332",
title = "Evolutionary Design and Experimental Validation of a Flexible Caudal Fin for Robotic Fish",
note = "Best Paper Award",
}
Review and recommend policies and procedures for the use of information technology resources.
Promote student learning and achievement by sustaining faculty in their development as teachers.
Mentor and advise first year students.
Advise students applying for Fulbright Awards.
Given strong student demand, I (and one of our EE faculty) initiated Missouri State University’s first robotics club.
Act upon curricular matters that are referred to it by departments within the college. The College Council approves departmental proposals, rejects and returns proposals to the originating department, or amends and approves proposals.
Attend recruitment events on the behalf of the college, and make recommendations to the dean regarding recruitment procedures.
Advise the new outreach program, which is directed at providing mentoring for local lower-income schools.
Represent my department at the college level diversity committee. A primary goal for the members of this committee is to improve the retention of students that are considered at risk for either dropping out or transferring. We improve retention through a variety of activities: poster sessions, scholarships, and picnics.
Coordinate ACM study chapter activities, include: scheduling speakers, organizing off-campus activities (e.g., competitions), and recruit volunteers to help at departmental events.
Attend special training sessions on proactive advising techniques so that I can better advise first generation computer science undergraduates. I currently advise ~75 CS students.
Coordinated monthly meetings for graduate students in the Department of Computer Science and Engineering, facilitated communication of Department news and policies, and organized graduate student service opportunities.
Act as a voting member of the GSRC, which establishes academic standards, coordinates graduate course offerings, determines admission standards and policies for financial awards, and evaluates Ph.D. qualifier examinations.
Act as a voting member at CSE department meetings.
Applied Sciences
Elsevier Robotics and Autonomous Systems
IEEE Transactions on Systems, Man and Cybernetics: Systems
IEEE Transactions on Robotics
Sage Adaptive Behavior
Sage International Journal of Advanced Robotic Systems
IEEE and ACM
Smart and Autonomous Systems
National Robotics Initiative
IIS Robust Intelligence
Led a tutorial in the use of simulation in evolutionary robotics.
Evo-ROS: Integrating Evolutionary Robotics and ROS
Moore, Jared M., Clark, Anthony J., Simon, Glen, McKinley, Philip K. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver, BC, Canada, September, 2017
Responsibilities: Co-organize a workshop that brought together experts from around the world to discuss the topic of simulation; specifically how we can improve the current state of simulation in ER.
The Twelfth European Conference on Artificial Life (ECAL), International Evolution of Physical Systems Workshop (EPS)
Taormina, Italy, September 2013
Responsibilities: Mentor one FLL team and judge at a regional competition.
Responsibilities: Coordinate and advise from three to six undergraduate and graduate students from different departments every semester.
Responsibilities: Organized and taught a week-long summer coding camp for middle school students.
Responsibilities: Presented my research and an explanation of evolutionary robotics to 22 high school students. I introduced a web-based evolutionary robotics simulation platform (BoxCar2D) to the students and in a hands-on laboratory session helped them answer several questions regarding the evolutionary robotics process.
Responsibilities: Co-mentored Mr. René Draschwandtner, a visiting Master’s student from the University of Applied Sciences in Austria. I worked with Mr. Draschwandtner, Dr. Jared Moore, and Dr. Philip McKinley to study locomotion and grasping behaviors for a snake-like robot using methods from evolutionary robotics.
Responsibilities: Presented 3D printing technologies and my lab’s research as part of outreach directed at undergraduates and the general public.
Responsibilities: Organized an open discussion regarding the application of state-of-the-art web technologies to evolutionary research and outreach projects.
Responsibilities: Presented an overview of evolutionary computation to a group of four high school students interested in STEM fields, and then facilitated their work as they conducted, wrote about, and presented results from their own evolutionary study in a day-long course.
Responsibilities: Mentored a local high school engineering instructor, Charles Payson. Over the course of his second summer in the program, Mr. Payson designed, implemented, and presented a web application used to teach evolutionary robotics concepts to K-12 students and the general public. I taught Mr. Payson web-programming skills as well as aided him in developing a curriculum for high school students.
Responsibilities: Introduced evolutionary robotics to approximately 20 high school students in a tutorial style. The tutorial was based on an interactive web-based simulation environment developed by myself and Jared M. Moore. Students conducted evolutionary experiments in which they evolved robots in simulation.
Responsibilities: Provided feedback to undergraduates presenting their research, and scored poster presentations for a competition.
Responsibilities: Mentored a local high school engineering instructor, Charles Payson. During a six-week program, I aided Mr. Payson in learning C++ programming, evolutionary algorithm development, and creating dynamic simulations. At the end of the program, I assisted Mr. Payson in translating his research into a robotics lesson plan using the VEX robotics platform.
Responsibilities: Mentored a local elementary school teacher, Adam Ford, who specializes in computers and robotics. Mr. Ford developed the Biolume environment, which demonstrates evolution ‘in-action’ using using simple robots. The Biolume project is an outreach exhibit aimed at demonstrating evolutionary principles to the general public.
Title: Lunar geologic compass for geologic mapping and surveying
Award: Student Support
Details: Awarded funds to conduct research with graduate and undergraduate students.
PIs: Matt McKay and Anthony J. Clark
Award: $6,000
Details: Funds were awarded to work on research during the summer term.
PI: Anthony J. Clark
Award: Quadro P6000
Details: NVIDIA awarded a powerful GPU to be used for deep learning research.
PI: Anthony J. Clark
Amount: $24,000
Details: Funds were utilized to purchase a 3D printer and a CNC mill that will be used by faculty and students in the Departments of Computer Science and Engineering.
PI: Anthony J. Clark
Amount: $168,231
Sponsor: NSF BEACON Center for the Study of Evolution in Action
PI: T. Soule (U. Idaho), Co-PIs: R. Heckendorn (U. Idaho), P. McKinley (MSU), J. Zhan (NCA&T), S. Harrison (NCA&T)
Amount: $305,000
Sponsor: NSF, Division Of Computer and Network Systems
PI: P. McKinley, Co-PIs: X. Tan, J. Boughman
Amount: $169,923
Sponsor: NSF BEACON Center for the Study of Evolution in Action
PI: X. Tan, Co-PIs: P. McKinley, J. Boughman
Amount: $110,642
Sponsor: NSF BEACON Center for the Study of Evolution in Action
PI: X. Tan, Co-PIs: P. McKinley, J. Boughman
Description: Review is a web-based platform for sharing dynamic visualizations. Code, Description
Description: Evolve-a-Robot is an interactive evolutionary robotics simulation. The project has two goals. The first is to expose K-12 students to evolution and evolutionary computation using an engaging and fun platform. Evolve-a-Robot does this by visualizing the evolution of robotic cars with an easy-to-use interface. And second, to expose enough of the adjustable parameters (i.e. genetic operators, and evolutionary configuration) to make the simulation useful for teaching evolutionary algorithms to undergraduate students. Code, Description
URL: http://adamwbrown.net/biolume-header1-jpg/
Description: The Biolume art exhibit is meant to captivate and inform the general public. The installation will comprise approximately 150 culptural robots that ‘evolve’ to better interact with patrons. Through interaction with the public, Biolume robots gain energy and are preferentially selected for reproduction to ‘replace’ less fit neighbors.