Andrew H. Fagg:
Publications


Dissertation

  A Computational Model of The Cortical Mechanisms Involved in Primate Grasping, Ph.D. Dissertation, Computer Science Department, University of Southern California, 1996

Journal Articles

  1. Ghazi, M. A., Shotande, M. O., Torbati, R. J., Skorup, J., O'Leary, S. O., Alcott, M., Smith, B. A., Prosser, L. A., Kolobe, T. H.-A., Fagg, A. H. (in preparation). Robotic Rehabilitation Device for Promoting Prone Locomotion Skills in Infants with Cerebral Palsy.

  2. Shotande, M. O., Skorup, J., O'Leary, S. O., Alcott, M., Smith, B. A., Prosser, L. A., Ghazi, M., Kolobe, T. H.-A., Fagg, A. H. (submitted). A Graphical User Interface for Individualized Locomotor Training of Infants With or at High Risk of Cerebral Palsy Using a Robotic Assistive Device

  3. McGovern, A., Ebert-Uphoff, I., Barnes, E. A., Bostrom, A., Cains, M. G., Davis, P., Demuth, J. L. Diochnos, D. I. Fagg, A. H. Tissot, P., Williams, J. K., Wirz, C. D. (2024) AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography. AI Magazine. DOI: 10.1002/aaai.12160

  4. Prosser, L. A., Skorup, J., Pierce, S. R., Jawad, A. F., Fagg, A. H., Kolobe, T. H.-A., Smith, B. A. (2023). Locomotor Learning in Infants at High Risk for Cerebral Palsy: A Study Protocol , Frontiers in Pediatrics / Pediatric Neurology, 11, DOI: 10.3389/fped.2023.891633

  5. Veirs, K. P., Fagg, A. H., Rippetoe, J., Baldwin, J. D., Haleem, A. M., Jeffries, L. M., Randall, K., Sisson, S. B., and Dionne, C. P. (2023). Effects of First Effects of Dancer-Specific Biomechanics on Adolescent Ballet Dancers' Posture En Pointe and Factors Related to Pointe Readiness: A Cross-Sectional Study Journal of Medical Problems of Performing Artists, 38(8):155–163, Sept, DOI: 10.21091/mppa.2023.3019

  6. Shotande, M. O., Veirs, K. P., Day, J. D., Ertl, W. J., Dionne, C. P. and Fagg, A. H. (2022). Comparing Temporospatial Performance During Brisk and Self-Paced Walking by Men With Osteomyoplastic Transfemoral Amputation and Controls Using Pressure and Muscle Activation Peak Times, Frontiers in Rehabilitation Sciences, 3, DOI: 10.3389/fresc.2022.848657, May

  7. Veirs, K. P., Fagg, A. H., Haleem, A. M., Jeffries, L. M., Randall, K., Sisson, S. B. and Dionne, C. P. (2022). Application of Biomechanical Foot Models to Evaluate Dance Movements Using Three Dimensional Motion Capture: A Review of the Literature Journal of Dance Medicine and Science, DOI: 10.12678/1089-313X.061522a, 26(2):69–86, June

  8. Chandrashekhar, R., Wang, H., Rippetoe, J., James, S., Fagg, A. H. and Kolobe, T. H.-A. (2022) The Impact of Cognition on Motor Learning and Skill Acquisition using a Robot Intervention in Infants with Cerebral Palsy, Frontiers in Robotics and AI, 9, DOI: 10.3389/frobt.2022.805258, February

  9. Dionne, C. P., Regens, J. L., Day, J. D., Fagg, A. H., Bryant, D. J., Veirs, K. P. and Ertl, W. J. (2022) Technical Report: Gait Performance at 2 Speeds and Carrying Capacity by Men with an Osteomyoplastic Transfemoral Limb and Comparable Controls, Journal of Prosthetics and Orthotics, 34(2):e109–e113, JPO14-59R1, DOI: 10.1097/JPO.0000000000000352

  10. Veirs, K. P., Baldwin, J. D., Fagg, A. H., Haleem, A. M., Jeffries, L., Randall, K., Sisson, S., and Dionne, C. P. (2021) Survey of Ballet Dance Instructors and Female Dancers Concerning Perception of Dance-Related Pain and Injury, Orthopaedic Physical Therapy Practice

  11. Veirs, K. P., Baldwin, J. D., Rippetoe, J., Fagg, A., Haleem, A., Jeffries, L., Randall, K., Sisson, S. and Dionne, C. P. (2020). Multi-Segment Assessment of Ankle and Foot Kinematics during Relevé Barefoot and En Pointe. Orthopaedic Physical Therapy Practice, 32(3):167–175

  12. Kolobe, T. H.-A., Fagg, A. H. (2019). Robot Reinforcement and Error-Based Movement Learning in Infants with and without Cerebral Palsy. Physical Therapy 99(6):677–688, DOI: 10.1093/ptj/pzz043

  13. Vaidya, M., Balasubramanian, K., Southerland, J., Badreldin, I., Eleryan, A., Shattuck, K., Gururangan, S. Slutzky, Osborne, L., Fagg, A. H., Oweiss, K. and Hatsopoulos, N. G. (2017) Emergent Coordination Underlying Learning to Reach-to-Grasp with a Brain-Machine Interface, Journal of Neurophysiology, electronically published, PMID:29357477, DOI: 10.1152/jn.00982.2016

  14. Balasubramanian, K., Vaidya, M., Southerland, J., Badreldin, I., Eleryan, A., Takahashi, K., Qian, K., Slutzky, M. W., Fagg, A. H., Oweiss, K. and Hatsopoulos, N. G. (2017) Changes in Cortical Network Connectivity with Long-term Brain-Machine Interface Exposure after Chronic Amputation, Nature Communications, 8 (1796), DOI: 10.1038/s41467-017-01909-2

  15. Xiao, R., Qi, X., Patino, A., Fagg, A. H., Kolobe, T. H.-A., Miller, D. P. and Ding, L. (2016) Characterization of Infant Mu Rhythm Immediately before Crawling: A High-Resolution EEG Study, NeuroImage, 146:47–57, PMID: 27847348, DOI: 10.1016/j.neuroimage.2016.11.007

  16. Eleryan, A., Vaidya, M., Southerland, J., Badreldin, I., Balasubramanian, K., Fagg, A. H., Hatsopoulos, N. G. and Oweiss, K. (2014) Tracking Single Units in Chronic, Large scale, Neural Recordings for Brain Machine Interface Applications, Frontiers in Neuroengineering, 7(23), PMCID: PMC4086297

  17. Shah, A., Barto, A. G., Fagg, A. H. (2013) A Dual Process Account of Coarticulation in Motor Skill Acquisition, Journal of Motor Behavior, 45(6):531–549 doi: 10.1080/00222895.2013.837423, PMID: 24116847

  18. Willett, F. R., Suminski, A. J., Fagg, A. H. and Hatsopoulos, N. G. (2013) Improving Brain-Machine Interface Performance by Decoding Intended Future Movements, Journal of Neural Engineering, 10(2):206011, April. PMCID: PMC4019387

  19. Suminski, A. J., Tkach, D. C., Fagg, A. H., and Hatsopoulos, N. G. (2010) Incorporating Feedback from Multiple Sensory Modalities Enhances Brain-Machine Interface Control,Journal of Neuroscience, 30(50):16777-16787, December, PMCID: PMC3046069

  20. Platt, R., Fagg, A. H., and Grupen, R. A. (2010) Null Space Grasp Control: Theory and Experiments, IEEE Transactions on Robotics, 26(2):282–295, April

  21. Fagg, A. H., Ojakangas, G., Miller, L., Hatsopoulos, N. (2009) Kinetic Trajectory Decoding Using Motor Cortical Ensembles , IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(5):487–496, PMID:19666343, doi: 10.1109/TNSRE.2009.2039398

  22. Ou, S., Fagg, A. H., Shenoy, P., Chen, L. (2009) Application of Reinforcement Learning in Multisensor Fusion Problems with Conflicting Control Objectives, Intelligent Automation and Soft Computing, 15(2):277–289

  23. Fagg, A. H., Hatsopoulos, N. G., de Lafuente, V., Moxon, K. A., Nemati, S., Rebesco, J. M., Romo, R., Solla, S. A., Reimer, J., Tkach, D., Pohlmeyer, E. A., and Miller L. E. (2007) Biomimetic brain machine interfaces for the control of movement, Journal of Neuroscience, 27(44):11842–11846, PMID: 17978021

  24. Morales, A., Sanz, P. J., del Pobil, A. P., and Fagg, A. H. (2006) Vision-based three-finger grasp synthesis constrained by hand geometry Robotics and Autonomous Systems, 54(6):419–512

  25. Brock, O., Fagg, A. H., Grupen, R. A., Karuppiah, D., Platt, R., Rosenstein, M., (2005), A Framework For Humanoid Control and Intelligence, International Journal of Humanoid Robotics, 2(3):301–336

  26. Morales, A., Chinellato, E., Fagg, A. H., del Pobil, A. P. (2004) Using Experience for Assessing Grasp Reliability, International Journal of Humanoid Robotics, 1(4):671-691

  27. Shah, A., Fagg, A. H., Barto, A. G. (2004) Cortical Involvement in the Recruitment of Wrist Muscles, Journal of Neurophysiology, 91:2445 - 2456. PMID: 14749314 (pdf version) (official JNP version)

  28. Fagg, A. H., Shah, A., Barto, A. G. (2002) A Computational Model of Muscle Recruitment for Wrist Movements, Journal of Neurophysiology, 88(6):3348-3358, PMID: 12466451 (pdf version)

  29. Marcos, L., Oliveira, A. F., Grupen, R. A., Wheeler, D. S., and Fagg, A. H. (2000), Tracing Patterns and Attention: Humanoid Robot Cognition IEEE Intelligent Systems 15 (4):70–75, July/August

  30. Barto, A. G., Fagg, A. H., Sitkoff, N., Houk, J. C. (1999) A Cerebellar Model of Timing and Prediction in the Control of Reaching, Neural Computation 11:565–594, PMID: 10085421

  31. Fagg, A. H., Arbib, M. A. (1998) Modeling Parietal-Premotor Interactions in Primate Control of Grasping, Neural Networks 11(7/8):1277–1303 (pdf version)

  32. Grafton, S. T., Fagg, A. H., Arbib, M. A. (1998) Dorsal Premotor Cortex and Conditional Movement Selection: A PET Functional Mapping Study, Journal of Neurophysiology, 79(2):1092–1097

  33. Grafton, S. T., Fagg, A. H., Arbib, M. A., Woods, R. (1996) Functional Anatomy of Pointing and Grasping in Humans, Cerebral Cortex, 6(2):226–237

  34. Arbib, M. A., Bischoff, A., Fagg, A. H., Grafton, S. T. (1995) Synthetic PET: Analyzing Large-Scale Properties of Neural Networks, Human Brain Mapping, 2:225–233

  35. Montgomery, J. F., Fagg, A. H., Bekey, G. A. (1995) The USC AFV-I: A Behavior-Based Entry in the 1994 International Aerial Robotics Competition, IEEE Expert: Intelligent Systems and Their Applications, 10 (2):16–22, April (pdf version)

  36. Fagg, A. H., Arbib, M. A. (1992) A Model of Primate Visual/Motor Conditional Learning, Journal of Adaptive Behavior, Summer, 1(1):3–37 (pdf version)

Art Exhibitions

  1. Brown, A., Fagg, A. H. (2017), Bion, Consciencia Cibêrnética [?], in São Paulo, Brazil, invited exhibition, June 8 – August 6

  2. Brown, A., Fagg, A. H. (2010), Bion, Emoção Art.ficial 5.0: Autonomia Cibernética, in São Paulo, Brazil, invited exhibition, July 1 – September 15

  3. Brown, A., Fagg, A. H. (2010), Bion, Kresege Art Museum, Michigan State University, September 15 – October 10

  4. Brown, A., Fagg, A. H. (2007–2010), Bion, Stephenson Research and Technology Center, University of Oklahoma, Norman, OK, May 1, 2007 – January 15, 2010

  5. Brown, A., Fagg, A. H. (2007), Bion, Singularity in the Communal Tide, Pierro Gallery, South Orange, NJ, May 13–July 15

  6. Archinal, A., Bleckley, S., Courtney, C., Cunningham, P., Gay J., Goddard, B., Gomez, J., Hunt, T. Renyer, J., Roman, M., Brown, A., Fagg, A. H. (2007), PulsePool, Boston Cyber-Arts Festival, Boston Museum of Science, Boston, MA, April 21–29; co-supervisor of this student project

  7. Brown, A., Fagg, A. H. (2006), Bion, Bridge Art Fair, Curated by: Rupert Ravens Contemporary, Miami, Florida, December

  8. Brown, A., Fagg, A. H. (2006–2007), Bion, Engaging Technology: A History & Future of Intermedia, Ball State University, November 16 – March 11

  9. Brown, A., Fagg, A. H. (2006), Bion, Newark Between Us, Newark, NJ, October 22–December 17

  10. Brown, A., Fagg, A. H. (2006), Bion, Living Arts of Tulsa, Tulsa, OK, September 7–28

  11. Brown, A., Fagg, A. H. (2006), Bion, 33rd International Conference and Exhibition on Computer Graphics and Interactive Techniques, Boston, MA, July 30–August 3

  12. Brown, A., Fagg, A. H. (2006), Bion, iDEAs Exhibition at the International Digital Media and Arts Association Conference, Miami University, Oxford, OH, April 6–8

  13. Brown, A., Fagg, A. H. (2006), Bion, Archival to Contemporary: Six Decades of the Sculptors Guild, Hillwood Art Museum, Long Island University, Brookville, NY, January 30–May 15

Refereed Conference and Workshop Publications

  1. Ghazi, M. A., Ding, L., Fagg, A. H., Kolobe, T. H.-A., Miller, D. P. (2017), Vision-Based Motion Capture System for Tracking Crawling Motions of Infants., Proceedings of the 2017 IEEE International Conference on Mechatronics and Automation, Electronically Published

  2. Patino, A., Fagg, A. H., Kolobe, T. H.-A., Miller, D. P. and Ding, L. (2017) Dynamic Spatio-Spectral Patterns of Rhythmic EEG in Infants, Proceedings of the 8th International IEEE/EMBS Conference on Neural Engineering (NER), Electronically Published

  3. Xiao, R., Qi, X., Fagg, A. H., Kolobe, T. H.-A., Miller, D. P. and Ding, L. (2015) Spectra of Infant EEG within the First Year of Life: A Pilot Study, Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS15), Electronically Published (ThFPoT14.7)

  4. Ghazi, M., Nash, M., Fagg, A. H., Ding, L., Kolobe, T. H.-A. and Miller, D. P. (2015) Novel Assistive Device for Teaching Crawling Skills to Infants, Proceedings of the 10th Conference on Field and Service Robotics, Electronically Published (paper #58).

    Physical publication: Ghazi, M., Nash, M., Fagg, A. H., Ding, L., Kolobe, T. H.-A. and Miller, D. (2016) Novel Assistive Device for Teaching Crawling Skills to Infants, Springer Tracts in Advanced Robotics (Bruno Siciliano and Oussama Khatib, Eds.), Volume 113, pp. 593-605. March 16. ISSN: 1610-7438

  5. Willett, F., Suminski, A. J., Fagg, A. H. and Hatsopoulos, N. G. (2014), Differences in Motor Cortical Representations of Movement Variables between Action Observation and Action Execution and Implications for Brain-Machine Interfaces, Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), WC11.29

  6. Suminski, A. J., Fagg, A. H., Willett, F., Bodenhamer, M. and Hatsopoulos, N. G. (2013) Online Adaptive Decoding of Intended Movements with a Hybrid Kinetic and Kinematic Brain Machine Interface, Proceedings of the 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1583–1586

  7. Eleryan, A., Vaidya, M., Southerland, J., Badreldin, I., Balasubramanian, K., Fagg, A. H., Hatsopoulos, N. G. and Oweiss, K. (2013) Tracking Chronically Recorded Single-Units in Cortically Controlled Brain Machine Interfaces, Proceedings of the 6th International IEEE EMBS Conference on Neural Engineering (NER), pp. 427–430

  8. Badreldin, I., Southerland, J., Vaidya, M., Eleryan, A., Balasubramanian, K., Fagg, A. H., Hatsopoulos, N. G. and Oweiss, K. (2013) Unsupervised Decoder Initialization for Brain-Machine Interfaces Using Neural State Space Dynamics, Proceedings of the 6th International IEEE EMBS Conference on Neural Engineering (NER), pp. 997–1000

  9. Palmer, T. J., Bodenhamer, M. and Fagg, A. H. (2012) Learning to Predict Action Outcomes in Continuous, Relational Environments, Proceedings of the International Conference on Development and Learning (ICDL)

  10. Willett, F. R., Suminski, A. J., Fagg, A. H. and Hatsopoulos, N. G. (2012), Compensating for Delays in Brain-Machine Interfaces by Decoding Intended Future Movement, Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC–12), FrA15.1, August

  11. Suminski, A. J., Willett, F. R., Fagg, A. H., Bodenhamer, M., and Hatsopoulos, N. G. (2011) Continuous Decoding of Intended Movements with a Hybrid Kinetic and Kinematic Brain Machine Interface, Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC–11), August

  12. Hatsopoulos, N. G., Suminski, A. J., and Fagg, A. H. (2011) Using Naturalistic Kinesthetic Feedback for Brain Machine Control, Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC–11), August

  13. Bodenhamer, M., Bleckley, S., Fennelly, D., Fagg, A. H., and McGovern, A. (2009) Spatio-Temporal Multi-Dimensional Relational Framework Trees,, In the Proceedings of the International Workshop on Spatial and Spatiotemporal Data Mining (ICDM), IEEE Conference on Data Mining, Miami, FL, Electronically Published

  14. Palmer, T. J. and Fagg, A. H. (2009) Learning Grasp Affordances with Variable Centroid Offsets,, Proceedings of the International Conference on Intelligent Robots and Systems, MoIIIT7.1, St. Louis, MO

  15. Fagg, A. H., Hatsopoulos, N. G., London, B., Reimber, J., Solla, S., Wang, D., Miller, L. E. (2009) Toward a Biomimetic, Bidirectional, Brain Machine Interface, Proceedings of the 31st Annual International IEEE EMBS Conference, Minneapolis, MI, Electronically Published

  16. Miller, L. E., Fagg, A. H., Hatsopoulos, N., Mussa-Ivaldi, F. A., Solla, S. (2009) Bidirectional Brain-Machine Interfaces: Sensory Fusion and Adaptive Maps (Workshop), Proceedings of the Spring Meeting on the Neural Control of Movement, electronically published

  17. Fagg, A. H., Hatsopoulos, N. G., Miller, L. E., (2007) M1 and the Dynamic Limb: Decoding Joint Torque for Prediction and Control, component of the Biomimetic Brain Machine Interfaces for the Control of Limb Movement Mini-symposium (Chair, L. E. Miller) at the Annual Meeting for the Society of Neuroscience, Presentation #650.2.

  18. Wang, D., Watson, B. T., Fagg, A. H. (2007) A Switching Control Approach to Haptic Exploration for Quality Grasps, Proceedings of the Robotics: Science & Systems 2007 Workshop on Sensing and Adapting to the Real World, Electronically Published

  19. Nemati, S., Yeary, M., Yu, T.-Y., Wang, Y., Zhai, Y. and Fagg, A. H., (2007) Spectral Signature Classification Using A Support Vector Classifier, Proceedings of the IEEE Instrumentation and Measurement Technology Conference, Warsaw, May

  20. Brown, A., Fagg, A. H. (2006), Is it alive? Sensor Networks and Art, (artist sketch) Proceedings of the 33rd International Conference and Exhibition on Computer Graphics and Interactive Techniques

  21. Platt, Jr., R., Grupen, R. A., Fagg, A. H. (2006), Learning Grasp Context Distinctions that Generalize, Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Electronically Published

  22. de Granville, C., Southerland, J., Fagg, A. H. (2006), Learning Grasp Affordances Through Human Demonstration, Proceedings of the International Conference on Development and Learning (ICDL'06), Electronically Published

  23. Platt, Jr., R., Grupen, R. A., Fagg, A. H. (2006), Improving Grasp Skills Using Schema Structured Learning, Proceedings of the International Conference on Development and Learning (ICDL'06), Electronically Published

  24. Brown, A., Fagg, A. H. (2006), Is it alive? Sensor Networks and Art, Proceedings of the International Digital Media and Arts Association Conference, Miami University, Oxford, OH

  25. Platt, Jr., R., Fagg, A. H., Grupen, R. A. (2005), Re-Using Schematic Grasping Policies, Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Electronically Published

  26. Rosenstein, M. T., Fagg, A. H., Ou, S, Grupen, R. A. (2005) User Intentions Funneled Through A Human-Robot Interface, Proceedings of the 10th International Conference on Intelligent User Interfaces, pp. 257–259

  27. Platt, Jr., R., Fagg, A. H., Grupen, R. A. (2004), Manipulation Gaits: Sequences of Grasp Control Tasks, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'04), pp. 801–806

  28. Rosenstein, M. T., Fagg, A. H., and Grupen, R. A. (2004), Robot Learning with Predictions of Operator Intent, In the Proceedings of the 2004 AAAI Fall Symposium on the Intersection of Cognitive Science and Robotics: From Interfaces to Intelligence, pp. 107–8. AAAI Press, Menlo Park, CA

  29. Morales, A., Chinellato, E., Fagg, A. H., del Pobil, A.P. (2004), An active learning approach for assessing robot grasp reliability, In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2004), Sendai, Japan, September, Electronically Published.

  30. Morales, A. , Chinellato, E., Sanz, P. J., Fagg, A. H., del Pobil, A. P. (2004), Vision based planar grasp synthesis and reliability assessment for a multifinger robot hand: a learning approach, In the International Conference on Intelligent Manipulation and Grasping (IMG04), Genoa, Italy, Electronically Published.

  31. Morales, A., Chinelalto, E., Sanz, P. J., Fagg, A. H., del Pobil, A.P. (2004), Learning to predict grasp reliability with a multifinger robot hand by using visual features, In IASTED International Conference on Artificial Intelligence and Soft Computing, Marbella, Spain, September

  32. Bluethmann, W., Ambrose, R., Diftler, M., Huber, E., Fagg, A. H., Rosenstein, M., Platt, R., Grupen, R., Breazeal, C., Brooks, A., Lockerd, A., Peters, R. A., Jenkins, O. C., Mataric, M., Bugajska, M. (2004) Building an Autonomous Humanoid Tool User, Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Electronically Published

  33. Platt, R., Brock, O., Fagg, A. H., Karuppiah, D., Rosenstein, M., Coelho, J., Huber, M., Piater, J., Wheeler, D., and Grupen, R. A. (2003), A Framework For Humanoid Control and Intelligence, Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Electronically Published

  34. Morales, A., Chinellato, E., Fagg, A. H., del Pobil, A. P. (2003), Using Experience for Assessing Grasp Reliability, Proceedings of the IEEE-RAS International Conference on Humanoid Robots, Electronically Published

  35. Morales, A., Chinellato, E., Fagg, A. H., del Pobil, A. P. (2003), Experimental Prediction of the Performance of Grasp Tasks from Visual Features, Proceedings of International Conference on Intelligent Robots and Systems (IROS'03), Electronically Published

  36. Platt, Jr., R., Fagg, A. H., Grupen, R. A. (2003), Extending Fingertip Grasping to Whole Body Grasping, Proceedings of International Conference on Robotics and Automation (ICRA'03), pp. 2677–2682

  37. Platt, Jr., R., Fagg, A. H., Grupen, R. A. (2002), Nullspace Composition of Control Laws for Grasping, Proceedings of the International Conference on Intelligent Robots and Systems (IROS'02), Electronically Published

  38. Wang, Y., Thibodeau, B., Fagg, A. H., Grupen, R. A. (2002), Learning Optimal Switching Policies for Path Tracking Tasks on a Mobile Robot, Proceedings of the International Conference on Intelligent Robots and Systems (IROS'02), Electronically Published

  39. Morales, A., Sanz, P. J., del Pobil, A. P., Fagg, A. H. (2002), An Experiment in Constraining Vision-Based Finger Contact Selection with Gripper Geometry, Proceedings of the International Conference on Intelligent Robots and Systems (IROS'02), Electronically Published

  40. Wheeler, D. S., Fagg, A. H., Grupen, R. A. (2002), Learning Prospective Pick and Place Behavior, Proceedings of the International Conference on Development and Learning (ICDL'02), Electronically Published

  41. Amstutz, P. and Fagg, A. H. (2002), Real Time Visualization of Robot State with Mobile Virtual Reality, Proceedings of the International Conference on Robotics and Automation (ICRA'02), pp. 241–247 (pdf version)

  42. Davis, J. A., Fagg, A. H., Levine, B. N. (2001), Wearable Computers as Packet Transport Mechanisms in Highly-Partitioned Ad-Hoc Networks, Proceedings of the International Symposium on Wearable Computing, Zurich, Switzerland, October 2001, pp. 141–148

  43. Fagg, A. H., Barto, A. G., Houk, J. C. (1998) Learning to Reach Via Corrective Movements, Proceedings of the Tenth Yale Workshop on Adaptive and Learning Systems, New Haven, CT, June 10-12, pp. 179-185

  44. Fagg, A. H., Sitkoff, N., Barto, A. G., Houk, J. C. (1997) Cerebellar Learning for Control of a Two-Link Arm in Muscle Space, Proceedings of the IEEE Conference on Robotics and Automation, May, pp. 2638-2644

  45. Fagg, A. H., Sitkoff, N., Barto, A. G., Houk, J. C. (1997) A Model of Cerebellar Learning for Control of Arm Movements Using Muscle Synergies, Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, July 10-11, pp. 6-12

  46. McGovern, A., Sutton, R. S., Fagg, A. H. (1997) Roles of Macro-Actions in Accelerating Reinforcement Learning, Grace Hopper Celebration of Women in Computing, pp. 13–18

  47. Fagg, A. H., Lotspeich, D. L., Hoff, J. Bekey, G. A. (1994) Rapid Reinforcement Learning for Reactive Control Policy Design for Autonomous Robots, Proceedings of the World Congress on Neural Networks, June, pp. II 118–26, San Diego, California (pdf version)

  48. Fagg, A. H., Lotspeich, D. L., Bekey, G. A. (1994) Reinforcement-Learning Approach to Reactive Control Policy Design for Autonomous Robots, Proceedings of the IEEE Conference on Robotics and Automation, May, pp. 39-44, San Diego, California

  49. Fagg, A. H., Lewis, M. A., Montgomery, J. F., Bekey, G. A. (1993) The USC Autonomous Flying Vehicle: an Experiment in Real-Time Behavior-Based Control, Proceedings of the IEEE Conference on Intelligent Robots and Systems, July, pp. 1173–1180, Yokohama, Japan (pdf version)

  50. Fagg, A. H., Fiser, J. (1993) Low Level Modeling of the Development of Directionally Selective Microcircuits in Cat Striate Cortex, IEEE Conference on Neural Networks, March, pp. 772–777, San Francisco, California

  51. Fagg, A. H., King, I. K., Lewis, M. A., Liaw, J.-S., Weitzenfeld, A. (1992) A Neural Network Based Testbed for Modeling Sensorimotor Integration in Robotic Applications, Proceedings of the International Joint Conference on Neural Networks, June, pp. I 86-91, Baltimore

  52. Lewis, M. A., Fagg, A. H., Solidum, A., Bekey, G. A. (1992) Genetic Programming Approach to the Construction of a Neural Network for Control of a Walking Robot, Proceedings of the IEEE Conference on Robotics and Automation, May, pp. 2618-2623, Nice, France

  53. Fagg, A. H., Lewis, M. A., Iberall, T., Bekey, G. (1991) R$^2$AD : Rapid Robotics Application Development Environment, Proceedings of the IEEE Conference on Robotics and Automation, April, pp. 1420–1426, Sacramento, California

  54. Taber, W. R., Deich, R., Simpson, P., Fagg, A. H. (1988) The Recognition of Orca Calls with a Neural Network, Proceedings of the Japan Conference on Fuzzy Logic

Book Chapters

  1. de Granville, C., Wang, D., Southerland, J., Platt, Jr. R., and Fagg, A. H. (2009), Grasping Affordances: Learning to Connect Vision to Hand Action, “The Path to Autonomous Robots; Essays in Honor of George A. Bekey” (Gaurav S. Sukhatme, Ed.), Springer, pp. 59–80

  2. Arbib, M. A., Fagg, A. H., and Grafton, S. T. (2002), Synthetic PET Imaging for Grasping: From Primate Neurophysiology to Human Behavior, in Explorative analysis and data modelling in functional neuroimaging, (F. Sommer and A. Wichert, Eds.), Cambridge MA: The MIT Press, pp. 231–250

  3. Houk, J. C., Fagg, A. H., Barto, A. G. (2002), Fractional Power Damping Model of Joint Motion, Progress in Motor Control: Structure-Function Relations in Voluntary Movements (M. Latash, Ed.), Vol. II, pp. 147–178

  4. Fagg, A. H., Weitzenfeld, A. (2002) A Model of Primate Visual-Motor Conditional Learning, NSL – Neural Simulation Language: Systems and Applications (A. Weitzenfeld, M. A. Arbib, and A. Alexander, Eds.), MIT Press (pdf version)

  5. Fagg, A. H., Lotspeich, D. L., Hoff, J. Bekey, G. A. (1998) Rapid Reinforcement Learning for Reactive Control Policy Design for Autonomous Robots, in Artificial Life in Robotics (T. Shibata and T. Fukuda, Eds.) (pdf version)
  6. Iberall, T., Fagg, A. H. (1996) Neural Network Models for Selecting Hand Shapes, in Hand and Brain: The Neurophysiology and Psychology of Hand Movements (A. M. Wing, P. Haggard, J. R. Flanagan Eds.), pp. 243-264, Academic Press, San Diego, CA

  7. Lewis, M. A., Fagg, A. H., Bekey, G. A. (1994) Genetic Algorithms for Gait Synthesis in a Hexapod Robot, Chapter 11 of Recent Trends in Mobile Robots (Y. Zheng Ed.), pp. 317-331, World Scientific Press

  8. Fagg, A. H. (1993) Reinforcement Learning for Robotic Reaching and Grasping, Chapter 14 of New Perspectives in the Control of the Reach to Grasp Movement (K. M. B. Bennett and U. Castiello, Eds.), pp. 281-308, North Holland Press (pdf version)

  9. Fagg, A. H. (1991) Developmental Robotics : A New Approach to the Specification of Robot Programs, Chapter 26 of Neural Networks in Robotics (G. A. Bekey and K. Y. Goldberg, Eds.), pp. 459-486, Kluwer Academic Publishers

Abstracts and Non-Refereed Conference Papers

  1. Wilson Reyes, M., Kurbanovas, A., Fagg, A. H., Thorncroft, C. D., Sulia, K. J., Brotzge, J. A. (2024), Generalized Visibility Estimation from Camera Images Using Deep Learning American Meteorological Society 104th Annual Meeting, Baltimore, MD, January 28 – February 1

  2. Marrero-Colominas, H. M., Shotande, M., Fagg, A. H., White, M., Tissot, P., McGovern, A. (2024), Estimating Uncertainty of Water Temperature Predictions for Cold-Stunning Events in the Laguna Madre American Meteorological Society 104th Annual Meeting, Baltimore, MD, January 28 – February 1

  3. McGovern, A., Gagne, D. J., Ebert-Uphoff, I., Bostrom, A., Wirz, C. D., Chase R., Fagg, A. H., and Barnes, E. A. (2023) Creating Personalized Learning Journeys for All Levels of Learning in AI with Applications to Weather and Climate. American Meteorological Society 103rd Annual Meeting, Denver, CO, January 8–12

  4. Wilson Reyes, M., Kurbanovas, A., Fagg, A. H., Thorncroft, C. D., Sulia, K. J., Brotzge, J. A. (2023), Comparative Visibility Estimation from New York State Mesonet Camera Images Using Deep Learning American Meteorological Society 103rd Annual Meeting, Denver, CO, January 8–12

  5. Ferrera, V., Rothenberger, J. C., Wilson Reyes, M., Sutter, C., Fagg, A. H., and Diochnos, D. I. (2023) Classifying Road Surface Conditions with Self-Trained Artificial Intelligence American Meteorological Society 103rd Annual Meeting, Denver, CO, January 8–12

  6. Brewer, A., Kolobe, T. H.-A., Jeffries, L. and Fagg, A. H. (2023) Dosing Effect of a Device Assisted Task-Specific Training Protocol, to appear in the American Physical Therapy Association Combined Sections Meeting, February, Abstrct #37949

  7. Muller, Z. and Fagg, A. H. (2022) A Generalized Bridge for Robot Operating System Messaging with a MBED Microcontroller. Code release: github

  8. Shotande, M. O., Veirs, K. P., Day, J. D., Ertl, W. J. J., Fagg A. H., Dionne, C. P. (2022) Gait Performance of Men with Osteomyoplastic Transfemoral Amputation and Controls using Novel Peak Analysis, College of Allied Health Research Day, March

  9. Huffman, N., Fagg A. H., Kolobe, T. H.-A. (2022) Automatically Identifying Developmental Micro-Milestones from Infant Motion Data, College of Allied Health Research Day, March

  10. Wagner, C., Fagg, A. H., Ghazi, M., Kolobe, T. H.-A. (2022) Comparing Strategies Used by Infants with and without Cerebral Palsy during Motor Learning, College of Allied Health Research Day, March

  11. Rippetoe, J., Chandrashekhar, R. Wang, H., James, S. A., Fagg, A. H., Kolobe, T. H.-A. (2022) How Cognition Impacts Motor Learning and Skill Acquisition Using a Robot Intervention for Cerebral Palsy, College of Allied Health Research Day, March

  12. Ghazi, M., Kolobe, T. H.-A., Fagg, A. H., Miller, D. P. (2022) MoViT: Monocular Vision-Based Tracking, College of Allied Health Research Day, March

  13. Kolobe, T. H.-A., Ding, L., Fagg, A. H., Miller, D. P. (2019) The Utility of EEG as a Measure of Motor Development and Intervention Outcomes, World Congress for Physical Therapy, May

  14. Kolobe, T. H.-A., Patino, A. Ding, L., Fagg, A. H., Miller, D. P. (2018) Electroencephalography and Infant motor proficiency during development of Prone Locomotion, Appears in the American Physical Therapy Association Combined Sections Meeting, New Orleans, LA, February 21-24, #2803944

  15. Twum-Ampofo, N., Porter, A., Johnson, L., Fagg, A. H., Kolobe, T. H.-A. (2018) Movement Proficiency and Center of Pressure in Infants with and without CP during Development of Prone Progression, Appears in the American Physical Therapy Association Combined Sections Meeting, New Orleans, LA, February 21-24, #2803881

  16. Kolobe T. H.-A. and Fagg, A. H. (2017) Robotic Reinforcement and Error-based Movement Learning during Skill Acquisition in Infants with and without risk for Cerebral Palsy, Developmental Medicine and Child Neurology, 59:76. doi:10.1111/dmcn.116_13511

  17. Twum-Ampofo, N. S., Kolobe, T. H.-A., Porter, A., Rauh-Johnson, L. and Fagg, A. H. (2017) Postural Control And Movement Proficiency In Infants With And Without Cerebral Palsy Using The Self Initiated Prone Progression Crawler-2, Appears in the American Physical Therapy Association Combined Sections Meeting, 29(1), San Antonio, TX, February 15-18, doi: 10.1097/PEP.000000000000035

  18. Miller D., Fagg, A. H., Ding, L., Kolobe, H.-A. and Ghazi, M. (2015) Robotic Crawling Assistance for Infants with Cerebral Palsy, Proceedings of the AAAI Workshop on Assistive Technologies Emerging from Artificial Intelligence Applied to Smart Environments, January

  19. Kolobe T. H.-A., Fagg, A. H., Pidcoe, P., Williams, P. (2015) Effectiveness of Reward- and Error-Based Movement Learning in Enhancing Self-Initiated Prone Locomotion in Infants with or at Risk for Cerebral Palsy. Proceedings of the The World Confederation of Physical Therapy Congress. Abstract A-613-0000-03707. Singapore. May. Selected as a State of the Art Presentation and awarded the Outstanding Platform Presentation Award.

  20. Kolobe T. H.-A., Fagg, A. H., Pidcoe, P., Brown, D., Bulanda, M. and Rauh, L. (2015) Development of prone locomotion in infants with or at risk for cerebral palsy. Appears in the Combined Section Meeting Conference of the American Physical Therapy Association, Indianapolis, February.

  21. Cox P. J., Kolobe T. H.-A., Fagg A. H. and Schmiedeberg T. (2015) Prone Locomotion in Infants With Down syndrome using the SIPPC: A Pilot Study. Appears in the Combined Section Meeting Conference of the American Physical Therapy Association, Indianapolis, February.

  22. Kolobe, T. H.-A. and Fagg, A. H. (2014) The effect of sensor robotic technology on the development of prone mobility in infants with or at risk for cerebral palsy. Journal of Developmental Medicine & Child Neurology (Special Issue: Abstracts of the American Academy for Cerebral Palsy and Developmental Medicine 68th Annual Meeting), 56(5):99

  23. Kolobe T. H.-A., Fagg A. H. and Ng, Y (2014). Comparison of the effect of robotic reinforced movement learning technology on the development of prone locomotion in infants with and without risk for cerebral palsy,. Appears in the Annual Meeting of the Child Neurology Society, Columbus OH, October.

  24. Kolobe, T. H.-A., Fagg, A. H, Pidcoe, P. and Miller D. (2014) The effect of robotic reinforced movement learning technology on the development of prone mobility in infants at low and high risk for cerebral palsy. Appears in the Annual Meeting for the Society of Neuroscience, Washington DC, November.

  25. Suminski, A. J., Fagg, A. H., Willett, F., Bodenhamer, M. and Hatsopoulos, N. G. (2013), Hybrid Online Adaptive Decoding of Intended Movements using a Feedback Error Learning Approach, Proceedings of the Society for Neuroscience Annual Meeting, 835.13/PP22

  26. Kolobe, T. H. A., Fagg A. H., Pidcoe P., Miller D. and Southerland J. (2013) Kinetic-Kinematic Patterns in Acquisition of Prone-Locomotion in Infants with/out Cerebral Palsy, Proceedings of the Fourth International Conference on Cerebral Palsy, Pisa, Italy, October 10–13, OP72

  27. Badreldin, I., Balasubramanian, K., Vaidya, M., Southerland, J., Fagg, A. H., Hatsopoulos, N. G. and Oweiss, K. (2013) Evaluation of Single Unit Error Contribution to Neural State Space Dynamics in Linear BMI Decoders, Proceedings of Computational and Systems Neuroscience (Cosyne), II-31

  28. Bodenhamer, M., Willett, F. R., Suminski, A. J., Hatsopoulos, N. G. and Fagg, A. H. (2012), A Feedback Error Learning Approach to Online-Adaptive Decoding for Dynamic Prosthetic Arm Control, Proceedings of Computational and Systems Neuroscience (Cosyne), I-38

  29. Willett, F. R., Fagg, A. H., Suminski, A. J. and Hatsopoulos, N. G. (2012), Improving neural control of a simulated arm by decoding intended future movement, Proceedings of Computational and Systems Neuroscience (Cosyne), III-39

  30. Catalino, T., Kolobe, T., McEwen, I., and Fagg, A. H. (2012). Development of Prone Locomotion in Infants Using an Assistive Device, Proceedings of the Combined Sections Meeting Conference of the American Association of Physical Therapy

  31. Sloan, A. M, Dodd, O. T., Houck, K., Palmer, T. J., Fagg, A. H. and Rennaker, R. L. (2011), Multi-Unit Responses in Behaving Rat Auditory Cortex Predict Frequency Discrimination Behavior, Society for Neuroscience Annual Meeting, 173.24, November

  32. Goossaert, E. and Fagg, A. H. (2009), A Corrective Movement Approach to Online Adaptive Decoders (poster), Proceedings of the Spring Meeting on the Neural Control of Movement, electronically published

  33. Hatsopoulos, N. G., Suminski, A., Tkach, D. and Fagg, A. H. (2009) Augmenting Brain-Machine Interfaces with Proprioceptive Feedback (poster), Proceedings of the Spring Meeting on the Neural Control of Movement, electronically published

  34. Tingle, D.T., Fagg, A.H., Rennaker, R.L. and Zee, M.C. (2008) Decoding Odor from the Piriform Cortex Using a Free-Paced Classifier, Society for Neuroscience Annual Meeting, student poster session

  35. Nemati, S. Fagg, A. H., Hatsopoulos, N., Miller, L. (2007) A Comparison of Linear and Kalman Filter Models for Arm Motion Prediction, Proceedings of the Spring Meeting on the Neural Control of Movement, Electronically Published

  36. Brown, A., Fagg, A. H. (2006), The Bion Sensor Network, Invited talk at Upgrade! International, November 30 – December 3

  37. Shah, A., Barto, A. G., Fagg, A. H. (2006) Biologically-Based Functional Mechanisms of Coarticulation, Proceedings of the Spring Meeting on the Neural Control of Movement, Electronically Published

  38. Goldberg, D., Fagg, A. H., Hatsopoulos, N., Ojakangas, G., Miller, L. (2006) A Kernel-Based Approach to Predicting Arm Motion from MI Activity, Proceedings of the Spring Meeting on the Neural Control of Movement, Electronically Published

  39. Fagg, A. H., Grupen, R. A., Rosenstein, M., and Sweeney, J. (2005), Intent Recognition as a Basis for Imitation Learning in Humanoid Robots, New England Manipulation Symposium, Electronically Published

  40. Fagg, A. H., Rosenstein, M. T., Platt, Jr., R., Grupen, R. A. (2004), Extracting User Intent in Mixed Initiative Teleoperator Control, Proceedings of the American Institute of Aeronautics and Astronautics Intelligent Systems Technical Conference, 2004-6309

  41. Ou, S., Karuppiah, D. R., Fagg, A. H. and Riseman, E. (2004), An Augmented Virtual Reality Interface for Assistive Monitoring of Smart Spaces, Proceedings of the IEEE International Conference on Pervasive Computing and Communications, p. 33. Supporting videos

  42. Fagg, A. H., Ou, S., Hedges, T. R., Brewer, M., Piantedosi M., Amstutz P., Hanson, A., Zhu, Z., Grupen, R., and Riseman, E. (2002), Human-Robot Interaction Through a Distributed Virtual Environment, Proceedings of the Workshop on Intelligent Virtual Environments and Human Augmentation (WIHAVE), Chapel Hill, NC, October 17–19. (pdf version). The full poster is also available.

  43. Shah, A., Fagg, A. H., and Barto, A. G. (2002) A Model of Wrist Movement Representation in Primary Motor Cortex, Proceedings of the Spring Meeting on the Neural Control of Movement, Naples, FL, Electronically Published Full Poster (pdf)

  44. Shah, A., Fagg, A. H., and Barto, A. G. (2001), A Computational Model of Muscle Recruitment for Wrist Movements, Proceedings of the Spring Meeting on the Neural Control of Movement, Sevilla, Spain, Electronically Published

  45. Fagg, A. H., Alamed, B., and Warwick, J. (2001), A Mobile Interactive Tour Guide: An Experiment in Wearable Computing, Five College Multimedia Fair, February 28

  46. Fagg, A. H., Shah, A., Barto, A. G. (2000) A Model of Wrist Movement Representation in Muscle and Primary Motor Cortex presented at the USC Symposium on Computational and Cognitive Neuroscience, Aug. 11-12, Los Angeles, CA

  47. Fagg, A. H., Zelevinsky, L., Barto, A. G., Houk, J. C. (1998) A Pulse-Step Model of Control for Arm Reaching Movements, Proceedings of the Spring Meeting on the Neural Control of Movement

  48. Fagg, A. H., Sitkoff, N., Barto, A. G., Houk, J. C. (1997) A Computational Model of Cerebellar Learning for Limb Control, Proceedings of the Spring Meeting on the Neural Control of Movement

  49. Lewis, M. A., Fagg, A. H., Bekey, G. A. (1994) Evolution of Complex Behaviors in Robotic Systems SPIE's Robotics and Machine Perception Newsletter, March, 3(1), pp. 1–6

  50. Fagg, A. H. (1993) Reinforcement Learning for Robotic Reaching and Grasping, Proceedings of the 1993 USC Workshop on Neural Architectures and Distributed AI: from Schema Assemblages to Neural Networks, Oct. 19-20, Los Angeles, California

  51. Fagg, A. H., Tillery, S. I. H., Terzuolo, C. A. (1992) Motion Velocity Profiles Influence the Perception of Hand Trajectories in the Absence of Vision, Proceedings of the 22nd Meeting of the Society for Neuroscience, October, p. 647.9, Anaheim, California

Workshop and Tutorial Presentations

  1. Platt, Jr., R., Fagg, A. H., Grupen, R. A. (2004), Learning Dexterous Manipulation Skills Using the Control Basis AAAI Fall Symposium on Real-life Reinforcement-Learning, Oct. 22–24

  2. del Pobil, A. P., Fagg, A. H. (2000) Robotics and Neuroscience, Tutorial presented at Intelligent Robots and Systems (IROS), Oct. 31, Takamatsu, Japan

  3. Fagg, A. H., Barto, A. G., Houk, J. C. (1998) Learning to Reach Using Crude Corrective Feedback presented at the NIPS workshop on Movement Primitives: Building Blocks for Learning Motor Control, Dec. 4, Breckenridge, CO

  4. Fagg, A. H., Zelevinsky, L., Barto, A. G., Houk, J. C. (1997) Using Crude Corrective Movements to Learn Accurate Motor Programs for Reaching, presented at the NIPS workshop on Can Artificial Cerebellar Models Compete to Control Robots, Dec. 5, Breckenridge, CO

Invited Talks

  1. Fagg, A. H. (2014), A Robotic Crawling Assistant for Children at Risk for Cerebral Palsy, Intelligent Robots and Systems (IROS) Workshop on Assistive Robotics for Individuals with Disabilities: HRI Issues and Beyond, Chicago, September 14.

  2. Fagg, A. H. (2013), Learning Grasp-Oriented Visual Representations through Interaction Arizona State University, February 15

  3. Fagg, A. H. (2007), A Structured Approach for Control and Learning of Humanoid Reaching and Grasping Skills Drury University. November 16

  4. Fagg, A. H., Watson, B., Wang, D., Southerland, J. (2006), Whole-Body Contact Sensing, Presentation, Dexterous Robotics Laboratory, NASA/Johnson Space Center, May 22

  5. Fagg, A. H. (2005), Predicting Arm Motion from Motorcortical Activity (talk and lab session), 5th International UJI Summer School on Robotics and Neuroscience, September, 19-23, 2005, Benicassim, Spain

  6. Fagg, A. H. (2001), Wearable Computers: A Changing (Inter)Face of Computing, talk presented at Sandia National Laboratories, Livermore, CA, August 9, 2001

Technical Reports

  1. Palmer, T. J., Bodenhamer, M. and Fagg, A. H. (2014), Multiple Instance Learning via Covariant Aggregation, Artificial Intelligence and Robotics Technical Report #1139, University of Oklahoma

  2. Bodenhamer, M., Palmer, T. J., Sutherland D. and Fagg, A. H. (2012), Grounding Conceptual Knowledge with Spatio-Temporal Multi-Dimensional Relational Framework Trees Artificial Intelligence and Robotics Technical Report #1138, University of Oklahoma

  3. de Granville, C., Fagg, A. H. (2008), Learning Grasp Affordances Through Human Demonstration, Artificial Intelligence and Robotics Technical Report #1137, University of Oklahoma

  4. Thibodeau, B. J., Fagg, A. H., Levine, B. N. (2004), Signal Strength Coordination for Cooperative Mapping Technical Report #04-64, Department of Computer Science, University of Massachusetts, Amherst

  5. Fagg, A. H. (2000), A Model of Muscle Geometry for a Two Degree-Of-Freedom Planar Arm Technical Report #00-03, Department of Computer Science, University of Massachusetts, Amherst 






































































 






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