A. Michael West Jr., originally from Rancho Cucamonga, CA, is a sixth-year PhD Candidate in Mechanical Engineering at the Massachusetts Institute of Technology (MIT). His research seeks to better control algorithms for robots involved in rehabilitation, and dexterous manipulation. He has investigated human upper-limb motor control and human hand manipulation during tool-use and physical interaction. By introducing new methods of analyzing human motor control and exploiting old methods on new experimental paradigms, his work leads to new insights on human motor control and perception. For his work, he has been awarded the MIT OGE Diversity Fellowship, UCEM Sloan Scholarship, GEM University Associate Fellowship, Ford Foundation Fellowship, Ben Gold Fellowship, Takeda Fellowship, and Accenture Fellowship.

Throughout his career, A. Michael West Jr. has been a passionate volunteer and mentor for students from underrepresented backgrounds through his work in STEM teaching and mentoring programs. This includes but is not limited to his previous positions as President of the National Society of Black Engineers Yale Chapter, Treasurer of the Academy of Courageous Minority Engineers at MIT, Treasurer of the Black Graduate Student Association at MIT, Pod Leader for the MIT Summer Research Program, and Graduate Diversity Ambassador at MIT. In his free time, Michael enjoys playing sports, video games, card/board games, watching movies and television. In the future, Michael hopes to continue to support and guide students of all backgrounds, especially those underrepresented, towards STEM fields, as a tenure-track professor.

Research Interests: Controls, Robotics, Rehabilitation, Dexterous Manipulation, Human Motor Control, Biomechanics, Physical Interaction, Sport

news

Apr 19, 2024	I successfully defended my PhD thesis earning me the title of Dr. West!
Jan 11, 2024	I was awarded the Johns Hopkins University (JHU) Provost's Postdoctoral Fellowship to fund my postdoctoral appointment at JHU for the 2024-2025 Academic Year!
Oct 28, 2023	I attended the 2023 Institute for Teaching and Mentoring.
Sep 26, 2023	I presented at the 2023 International Consortium for Rehabilitation Robotics (ICORR).
Sep 13, 2023	I was featured on the front page of MIT's website!

selected publications

2023

1. 

   The Study of Complex Manipulation via Kinematic Hand Synergies: The Effects of Data Pre-Processing

   A. Michael West Jr., Federico Tessari, and Neville Hogan

   IEEE ... International Conference on Rehabilitation Robotics : [proceedings], Sep 2023

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   The study of kinematic hand synergies through matrix decomposition techniques, such as singular value decomposition, supports the theory that humans might control a subspace of predefined motions during manipulation tasks. These subspaces are often referred to as synergies. However, different data pre-processing methods lead to quantitatively different conclusions about these synergies. In this work, we shed light on the role of data pre-processing on the study of hand synergies by analyzing both numerical simulation and real kinematic data from a complex manipulation task, i.e., piano playing. The results obtained suggest that centering the data, by removing the mean, appears to be the most appropriate preprocessing technique for studying kinematic hand synergies.

   @article{West2023,
     author = {West Jr., A. Michael and Tessari, Federico and Hogan, Neville},
     doi = {10.1109/ICORR58425.2023.10304710},
     issn = {19457901},
     journal = {IEEE ... International Conference on Rehabilitation Robotics : [proceedings]},
     keywords = {Human Hand, Manipulation, Rehabilitation, Singular Value Decomposition, Synergy, Task Complexity},
     month = sep,
     pages = {1--6},
     pmid = {37941248},
     title = {{The Study of Complex Manipulation via Kinematic Hand Synergies: The Effects of Data Pre-Processing}},
     volume = {2023},
     year = {2023},
     videosummary = {https://www.youtube.com/watch?v=J1Xp5Ekl6JQ}
   }

2022

1. 

   Dynamic Primitives Limit Human Force Regulation During Motion

   A. Michael West Jr., James Hermus, Meghan E. Huber, and 3 more authors

   IEEE Robotics and Automation Letters, Apr 2022

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   Humans excel at physical interaction despite long feedback delays and low-bandwidth actuators. Yet little is known about how humans manage physical interaction. A quantitative understanding of how they do is critical for designing machines that can safely and effectively interact with humans, e.g. amputation prostheses, assistive exoskeletons, therapeutic rehabilitation robots, and physical human-robot collaboration. To facilitate applications, this understanding should be in the form of a simple mathematical model that not only describes humans’ capabilities but also their limitations. In robotics, hybrid control allows simultaneous, independent control of both motion and force and it is often assumed that humans can modulate force independent of motion as well. This letter experimentally tested that assumption. Participants were asked to apply a constant 5 N force on a robot manipulandum that moved along an elliptical path. After initial improvement, force errors quickly plateaued, despite practice and visual feedback. Within-trial analyses revealed that force errors varied with position on the ellipse, rejecting the hypothesis that humans have independent control of force and motion. The findings are consistent with a feed-forward motion command composed of two primitive oscillations acting through mechanical impedance to evoke force.

   @article{West2022,
     author = {West Jr., A. Michael and Hermus, James and Huber, Meghan E. and Maurice, Pauline and Sternad, Dagmar and Hogan, Neville},
     journal = {IEEE Robotics and Automation Letters},
     month = apr,
     number = {2},
     pages = {2391--2398},
     publisher = {Institute of Electrical and Electronics Engineers Inc.},
     title = {{Dynamic Primitives Limit Human Force Regulation During Motion}},
     volume = {7},
     year = {2022},
     videosummary = {https://www.youtube.com/watch?v=RRfWe9KVSDY}
   }

2. 

   Role of path information in visual perception of joint stiffness

   A. Michael West Jr., Meghan E. Huber, and Neville Hogan

   PLoS Computational Biology, Nov 2022

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   Humans have an astonishing ability to extract hidden information from the movement of others. In previous work, subjects observed the motion of a simulated stick-figure, two-link planar arm and estimated its stiffness. Fundamentally, stiffness is the relation between force and displacement. Given that subjects were unable to physically interact with the simulated arm, they were forced to make their estimates solely based on observed kinematic information. Remarkably, subjects were able to correctly correlate their stiffness estimates with changes in the simulated stiffness, despite the lack of force information. We hypothesized that subjects were only able to do this because the controller used to produce the simulated arm’s movement, composed of oscillatory motions driving mechanical impedances, resembled the controller humans use to produce their own movement. However, it is still unknown what motion features subjects used to estimate stiffness. Human motion exhibits systematic velocity-curvature patterns, and it has previously been shown that these patterns play an important role in perceiving and interpreting motion. Thus, we hypothesized that manipulating the velocity profile should affect subjects’ ability to estimate stiffness. To test this, we changed the velocity profile of the simulated two-link planar arm while keeping the simulated joint paths the same. Even with manipulated velocity signals, subjects were still able to estimate changes in simulated joint stiffness. However, when subjects were shown the same simulated path with different velocity profiles, they perceived motions that followed a veridical velocity profile to be less stiff than that of a non-veridical profile. These results suggest that path information (displacement) predominates over temporal information (velocity) when humans use visual observation to estimate stiffness.

   @article{West2024,
     author = {West Jr., A. Michael and Huber, Meghan E. and Hogan, Neville},
     journal = {PLoS Computational Biology},
     month = nov,
     number = {11},
     pages = {e1010729},
     publisher = {Public Library of Science},
     title = {{Role of path information in visual perception of joint stiffness}},
     volume = {18},
     year = {2022},
     videosummary = {https://www.youtube.com/watch?v=-bbXeqru7b0}
   }