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1. Stability and predictability in dynamically complex physical interactions.

   Bazzi, Salah; Ebert, Julia; Hogan, Neville; Sternad, Dagmar (April 2018, International Conference on Robotics and Automation (ICRA2018))

   This study examines human control of physical interaction with objects that exhibit complex (nonlinear, chaotic, underactuated) dynamics. We hypothesized that humans exploited stability properties of the human-object interaction. Using a simplified 2D model for carrying a “cup of coffee”, we developed a virtual implementation to identify human control strategies. Transporting a cup of coffee was modeled as a cart with a suspended pendulum, where humans moved the cart on a horizontal line via a robotic manipulandum. The specific task was to transport the cart-pendulum system to a target, as fast as possible, while accommodating assistive and resistive perturbations. To assess trajectory stability, we applied contraction analysis. We showed that when the perturbation was assistive, humans absorbed the perturbation by controlling cart trajectories into a contraction region prior to the perturbation. When the perturbation was resistive, subjects passed through a contraction region following the perturbation. Entering a contraction region stabilizes performance and makes the dynamics more predictable. This human control strategy could inspire more robust control strategies for physical interaction in robots. 

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2. Multi-modal Interactive Perception in Human Control of Complex Objects

   https://doi.org/10.1109/ICRA48891.2023.10160375  

   Nayeem, Rashida; Bazzi, Salah; Sadeghi, Mohsen; Razavian, Reza Sharif; Sternad, Dagmar (May 2023, IEEE International Conference on Robotics and Automation (ICRA 2023),)

   Tactile sensing has been increasingly utilized in robot control of unknown objects to infer physical properties and optimize manipulation. However, there is limited understanding about the contribution of different sensory modalities during interactive perception in complex interaction both in robots and in humans. This study investigated the effect of visual and haptic information on humans’ exploratory interactions with a ‘cup of coffee’, an object with nonlinear internal dynamics. Subjects were instructed to rhythmically transport a virtual cup with a rolling ball inside between two targets at a specified frequency, using a robotic interface. The cup and targets were displayed on a screen, and force feedback from the cup-andball dynamics was provided via the robotic manipulandum. Subjects were encouraged to explore and prepare the dynamics by “shaking” the cup-and-ball system to find the best initial conditions prior to the task. Two groups of subjects received the full haptic feedback about the cup-and-ball movement during the task; however, for one group the ball movement was visually occluded. Visual information about the ball movement had two distinctive effects on the performance: it reduced preparation time needed to understand the dynamics and, importantly, it led to simpler, more linear input-output interactions between hand and object. The results highlight how visual and haptic information regarding nonlinear internal dynamics have distinct roles for the interactive perception of complex objects. 

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3. Frequency-dependent force direction elucidates neural control of balance

   https://doi.org/10.1186/s12984-021-00907-2  

   Shiozawa, Kaymie; Lee, Jongwoo; Russo, Marta; Sternad, Dagmar; Hogan, Neville (September 2021, Journal of NeuroEngineering and Rehabilitation)

   Abstract BackgroundMaintaining upright posture is an unstable task that requires sophisticated neuro-muscular control. Humans use foot–ground interaction forces, characterized by point of application, magnitude, and direction to manage body accelerations. When analyzing the directions of the ground reaction forces of standing humans in the frequency domain, previous work found a consistent pattern in different frequency bands. To test whether this frequency-dependent behavior provided a distinctive signature of neural control or was a necessary consequence of biomechanics, this study simulated quiet standing and compared the results with human subject data. MethodsAiming to develop the simplest competent and neuromechanically justifiable dynamic model that could account for the pattern observed across multiple subjects, we first explored the minimum number of degrees of freedom required for the model. Then, we applied a well-established optimal control method that was parameterized to maximize physiologically-relevant insight to stabilize the balancing model. ResultsIf a standing human was modeled as a single inverted pendulum, no controller could reproduce the experimentally observed pattern. The simplest competent model that approximated a standing human was a double inverted pendulum with torque-actuated ankle and hip joints. A range of controller parameters could stabilize this model and reproduce the general trend observed in experimental data; this result seems to indicate a biomechanical constraint and not a consequence of control. However, details of the frequency-dependent pattern varied substantially across tested control parameter values. The set of parameters that best reproduced the human experimental results suggests that the control strategy employed by human subjects to maintain quiet standing was best described by minimal control effort with an emphasis on ankle torque. ConclusionsThe findings suggest that the frequency-dependent pattern of ground reaction forces observed in quiet standing conveys quantitative information about human control strategies. This study’s method might be extended to investigate human neural control strategies in different contexts of balance, such as with an assistive device or in neurologically impaired subjects. 

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4. Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance

   https://doi.org/10.15607/RSS.2018.XIV.046  

   Kalinowska, Aleksandra; Fitzsimons, Kathleen; Dewald, Julius; Murphey, Todd (June 2018, Robotics: science and systems)

   We propose a novel criterion for evaluating user input for human-robot interfaces for known tasks. We use the mode insertion gradient (MIG)—a tool from hybrid control theory—as a filtering criterion that instantaneously assesses the impact of user actions on a dynamic system over a time window into the future. As a result, the filter is permissive to many chosen strategies, minimally engaging, and skill-sensitive—qualities desired when evaluating human actions. Through a human study with 28 healthy volunteers, we show that the criterion exhibits a low, but significant, negative correlation between skill level, as estimated from task-specific measures in unassisted trials, and the rate of controller intervention during assistance. Moreover, a MIG-based filter can be utilized to create a shared control scheme for training or assistance. In the human study, we observe a substantial training effect when using a MIG-based filter to perform cart-pendulum inversion, particularly when comparing improvement via the RMS error measure. Using simulation of a controlled spring-loaded inverted pendulum (SLIP) as a test case, we observe that the MIG criterion could be used for assistance to guarantee either task completion or safety of a joint human-robot system, while maintaining the system’s flexibility with respect to user-chosen strategies. 

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5. Body Mechanics, Optimality, and Sensory Feedback in the Human Control of Complex Objects

   https://doi.org/10.1162/neco_a_01576  

   Razavian, Reza Sharif; Sadeghi, Mohsen; Bazzi, Salah; Nayeem, Rashida; Sternad, Dagmar (April 2023, Neural Computation)

   Abstract Humans are adept at a wide variety of motor skills, including the handling of complex objects and using tools. Advances to understand the control of voluntary goal-directed movements have focused on simple behaviors such as reaching, uncoupled to any additional object dynamics. Under these simplified conditions, basic elements of motor control, such as the roles of body mechanics, objective functions, and sensory feedback, have been characterized. However, these elements have mostly been examined in isolation, and the interactions between these elements have received less attention. This study examined a task with internal dynamics, inspired by the daily skill of transporting a cup of coffee, with additional expected or unexpected perturbations to probe the structure of the controller. Using optimal feedback control (OFC) as the basis, it proved necessary to endow the model of the body with mechanical impedance to generate the kinematic features observed in the human experimental data. The addition of mechanical impedance revealed that simulated movements were no longer sensitively dependent on the objective function, a highly debated cornerstone of optimal control. Further, feedforward replay of the control inputs was similarly successful in coping with perturbations as when feedback, or sensory information, was included. These findings suggest that when the control model incorporates a representation of the mechanical properties of the limb, that is, embodies its dynamics, the specific objective function and sensory feedback become less critical, and complex interactions with dynamic objects can be successfully managed. 

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