James Hermus

As a researcher, James develops descriptive models of human motor control in tasks that involve substantial force and motion – physical interaction tasks. His work stems from a unique perspective heavily grounded in physical modeling and control theory. By studying the quirks of the human motor system, his prior research has provided insight into the control mechanisms behind human physical interaction. His current research, as part of the Ambizione project, aims to ask further scientific questions about how humans manage contact, and engineering questions about how these ideas can be applied to robotics.

Prior to the Ambizione James was a Postdoctoral Researcher in the Robot Learning & Interaction Group and the Human-centered Robotics and AI group at the Idiap Research Institute, working on manipulation research under the direction of Dr. Sylvain Calinon and Dr. Emmanuel Senft. One of his primary projects was related to solid sampling for automated chemistry in collaboration with Swiss Cat+ at EPFL. Prior to this, he was a Postdoctoral Researcher in the Learning Algorithms and Systems (LASA) Laboratory at EPFL, under the supervision of Professor Aude Billard.

James earned his PhD in Mechanical Engineering from MIT as part of the Newman Laboratory, where he conducted research under the guidance of Professor Neville Hogan. His dissertation research investigated human physical interaction during circularly constrained motion–turning a crank. The focus of his work was to understand fundamentals about how humans manage physical interaction to improve rehabilitation and robotics.

Originally from Wisconsin, he earned his bachelor’s degree in Biomedical Engineering with an Honors in Research at the University of Wisconsin – Madison in 2016. During his undergraduate studies, he worked in the Neuromuscular Biomechanics lab of Professor Darryl Thelen where he helped in the design and testing of a novel sensor for measuring tendon stress. James is especially interested in research that will aid individuals with disabilities.

Outside of research James enjoys rock climbing, ski touring, and working on projects. As a graduate student he was heavily involved with the MIT Outing Club (MITOC) and MakerWorkShop.

MIT News feature: Learning challenges shape a mechanical engineer’s path

Idiap news: SNSF Ambizione grant awarded to James Hermus

Research Interests

Physical interaction -- tasks with substantial force and motion
Kinematic redundancy
System identification of mechanical impedance

News

Sep 1, 2026	Today I begin as an SNSF Ambizione Fellow and Principal Investigator at the Idiap Research Institute, marking the official start of my four-year project, Confronting the Paradox of Human Motor Control – Leveraging Subtractive and Generative Models, in collaboration with Sylvain Calinon (Idiap) and Auke Ijspeert (EPFL). I am delighted that Andrés Blanco joins as my first PhD student.
Jul 24, 2026	Visited Boston to celebrate the retirement of my doctoral advisor, Professor Neville Hogan. It was a pleasure presenting a bit about my research direction, catching up with former colleagues from the Newman Laboratory, and meeting many of the other students Neville has advised across four decades.
Jul 14, 2026	Congratulations to Victor Dubois and Zixu Lu on completing their Master’s projects — Victor on diffusing elementary dynamic actions, and Zixu on ergodic search over stiffness trajectories. It was a pleasure working with them both.
Jun 16, 2026	Hosted Steve Heim for a talk at Idiap. A genuine pleasure — his work on locomotion with central pattern generators connects closely to questions we are asking in the Ambizione project.
Jan 13, 2026	Hosted Johannes Lachner for a talk at Idiap on his recent work in contact-rich manipulation.
Jan 12, 2026	Welcoming Adrian Prados Carrasco, visiting PhD student from Universidad Carlos III de Madrid, who joins us at Idiap to work on submovement decomposition.

Selected Publications

Brownian Processes in Human Motor Control Support Descending Neural Velocity Commands

Federico Tessari, James Hermus, Rika Sugimoto-Dimitrova, and Neville Hogan

Scientific Reports 2024

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The motor neuroscience literature suggests that the central nervous system may encode some motor commands in terms of velocity. In this work, we tackle the question: what consequences would velocity commands produce at the behavioral level? Considering the ubiquitous presence of noise in the neuromusculoskeletal system, we predict that velocity commands affected by stationary noise would produce “random walks”, also known as Brownian processes, in position. Brownian motions are distinctively characterized by a linearly growing variance and a power spectral density that declines in inverse proportion to frequency. This work first shows that these Brownian processes are indeed observed in unbounded motion tasks e.g., rotating a crank. We further predict that such growing variance would still be present, but bounded, in tasks requiring a constant posture e.g., maintaining a static hand position or quietly standing. This hypothesis was also confirmed by experimental observations. A series of descriptive models are investigated to justify the observed behavior. Interestingly, one of the models capable of accounting for all the experimental results must feature forward-path velocity commands corrupted by stationary noise. The results of this work provide behavioral support for the hypothesis that humans plan the motion components of their actions in terms of velocity.
@article{Tessari_2024, author = {Tessari, Federico and Hermus, James and Sugimoto-Dimitrova, Rika and Hogan, Neville}, title = {Brownian Processes in Human Motor Control Support Descending Neural Velocity Commands}, journal = {Scientific Reports}, volume = {14}, number = {1}, pages = {8341}, year = {2024}, doi = {10.1038/s41598-024-58380-5}, }
Dynamic Primitives in Constrained Action: Systematic Changes in the Zero-Force Trajectory

James Hermus, Joseph Doeringer, Dagmar Sternad, and Neville Hogan

Journal of Neurophysiology 2024

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Humans substantially outperform robotic systems in tasks that require physical interaction, despite seemingly inferior muscle bandwidth and slow neural information transmission. The control strategies that enable this performance remain poorly understood. To bridge that gap, this study examined kinematically constrained motion as an intermediate step between the widely studied unconstrained motions and sparsely studied physical interactions. Subjects turned a horizontal planar crank in two directions (clockwise and counterclockwise) at three constant target speeds (fast, medium, and very slow) as instructed via visual display. With the hand constrained to move in a circle, nonzero forces against the constraint were measured. This experiment exposed two observations that could not result from mechanics alone but may be attributed to neural control composed of dynamic primitives. A plausible mathematical model of interactive dynamics (mechanical impedance) was assumed and used to subtract peripheral neuromechanics. This method revealed a summary of the underlying neural control in terms of motion, a zero-force trajectory. The estimated zero-force trajectories were approximately elliptical and their orientation differed significantly with turning direction; that is consistent with control using oscillations to generate an elliptical zero-force trajectory. However, for periods longer than 2-5 s, motion can no longer be perceived or executed as periodic. Instead, it decomposes into a sequence of submovements, manifesting as increased variability. These quantifiable performance limitations support the hypothesis that humans simplify this constrained-motion task by exploiting at least three primitive dynamic actions: oscillations, submovements, and mechanical impedance.

NEW & NOTEWORTHY: Control using primitive dynamic actions may explain why human performance is superior to robots despite seemingly inferior wetware; however, this also implies limitations. For a crank-turning task, this work quantified two such informative limitations. Force was exerted even though it produced no mechanical work, the underlying zero-force trajectory was roughly elliptical, and its orientation differed with turning direction, evidence of oscillatory control. At slow speeds, speed variability increased substantially, indicating intermittent control via submovements.
@article{Hermus_2024, author = {Hermus, James and Doeringer, Joseph and Sternad, Dagmar and Hogan, Neville}, title = {Dynamic Primitives in Constrained Action: Systematic Changes in the Zero-Force Trajectory}, journal = {Journal of Neurophysiology}, volume = {131}, number = {1}, pages = {1-15}, year = {2024}, doi = {10.1152/jn.00082.2023}, note = {PMID: 37820017}, }
Exploiting Redundancy to Facilitate Physical Interaction

James Hermus, Johannes Lachner, David Verdi, and Neville Hogan

IEEE Transactions on Robotics 2022

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The control of kinematically redundant robots is often approached using nullspace projection, which requires precise models and can be computationally challenging. Humans have many more degrees of freedom than are required to accomplish their tasks, but given neuromechanical limitations, it seems unlikely that biology relies on precise models or complex computation. An alternative biologically inspired approach leverages the compositionality of mechanical impedance. In theory, nullspace projection eliminates any conflict between two tasks. In contrast, superposition of task-space impedance and a full-rank joint-space impedance may impose a task conflict. This work compared nullspace projection with impedance superposition during unconstrained motion and forceful physical interaction. In practice, despite their theoretical differences, we did not observe a substantial influence of the nullspace projector weighting matrix. We found that nullspace projection and impedance superposition both resulted in measurable task conflict. Remarkably, when the dimensionality of the nullspace was increased, impedance superposition was comparable to nullspace projection.
@article{Hermus_2022, author = {Hermus, James and Lachner, Johannes and Verdi, David and Hogan, Neville}, doi = {10.1109/TRO.2021.3086632}, journal = {IEEE Transactions on Robotics}, number = {1}, pages = {599-615}, title = {Exploiting Redundancy to Facilitate Physical Interaction}, volume = {38}, year = {2022}, }
Separating Neural Influences from Peripheral Mechanics: the Speed-Curvature Relation in Mechanically Constrained Actions

James Hermus, Joseph Doeringer, Dagmar Sternad, and Neville Hogan

Journal of Neurophysiology 2020

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While the study of unconstrained movements has revealed important features of neural control, generalizing those insights to more sophisticated object manipulation is challenging. Humans excel at physical interaction with objects, even when those objects introduce complex dynamics and kinematic constraints. This study examined humans turning a horizontal planar crank (radius 10.29 cm) at their preferred and three instructed speeds (with visual feedback), both in clockwise and counterclockwise directions. To explore the role of neuromechanical dynamics, the instructed speeds covered a wide range: fast (near the limits of performance), medium (near preferred speed), and very slow (rendering dynamic effects negligible). Because kinematically constrained movements involve significant physical interaction, disentangling neural control from the influences of biomechanics presents a challenge. To address it, we modeled the interactive dynamics to “subtract off” peripheral biomechanics from observed force and kinematic data, thereby estimating aspects of underlying neural action that may be expressed in terms of motion. We demonstrate the value of this method: remarkably, an approximately elliptical path emerged, and speed minima coincided with curvature maxima, similar to what is seen in unconstrained movements, even though the hand moved at nearly constant speed along a constant-curvature path. These findings suggest that the neural controller takes advantage of peripheral biomechanics to simplify physical interaction. As a result, patterns seen in unconstrained movements persist even when physical interaction prevents their expression in hand kinematics. The reemergence of a speed-curvature relation indicates that it is due, at least in part, to neural processes that emphasize smoothness and predictability.

NEW & NOTEWORTHY: Physically interacting with kinematic constraints is commonplace in everyday actions. We report a study of humans turning a crank, a circular constraint that imposes constant hand path curvature and hence should suppress variations of hand speed due to the power-law speed-curvature relation widely reported for unconstrained motions. Remarkably, we found that, when peripheral biomechanical factors are removed, a speed-curvature relation reemerges, indicating that it is, at least in part, of neural origin.
@article{Hermus_2020, author = {Hermus, James and Doeringer, Joseph and Sternad, Dagmar and Hogan, Neville}, doi = {10.1152/jn.00536.2019}, eprint = {https://doi.org/10.1152/jn.00536.2019}, journal = {Journal of Neurophysiology}, note = {PMID: 32159419}, number = {5}, pages = {1870-1885}, title = {Separating Neural Influences from Peripheral Mechanics: the Speed-Curvature Relation in Mechanically Constrained Actions}, volume = {123}, year = {2020}, }
Gauging Force by Tapping Tendons

Jack A. Martin, Scott C. E. Brandon, Emily M. Keuler, James R. Hermus, Alexander C. Ehlers, Daniel J. Segalman, Matthew S. Allen, and Darryl G. Thelen

Nature Communications 2018

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Muscles are the actuators that drive human movement. However, despite many decades of work, we still cannot readily assess the forces that muscles transmit during human movement. Direct measurements of muscle–tendon loads are invasive and modeling approaches require many assumptions. Here, we introduce a non-invasive approach to assess tendon loads by tracking vibrational behavior. We first show that the speed of shear wave propagation in tendon increases with the square root of axial stress. We then introduce a remarkably simple shear wave tensiometer that uses micron-scale taps and skin-mounted accelerometers to track tendon wave speeds in vivo. Tendon wave speeds are shown to modulate in phase with active joint torques during isometric exertions, walking, and running. The capacity to non-invasively assess muscle–tendon loading can provide new insights into the motor control and biomechanics underlying movement, and could lead to enhanced clinical treatment of musculoskeletal injuries and diseases.
@article{Martin_2018, author = {Martin, Jack A. and Brandon, Scott C. E. and Keuler, Emily M. and Hermus, James R. and Ehlers, Alexander C. and Segalman, Daniel J. and Allen, Matthew S. and Thelen, Darryl G.}, doi = {10.1038/s41467-018-03797-6}, isbn = {2041-1723}, journal = {Nature Communications}, number = {1}, pages = {1592}, title = {Gauging Force by Tapping Tendons}, ty = {JOUR}, volume = {9}, year = {2018}, }