Neville Hogan

Humans have a remarkable ability to manage physical interaction despite a complex musculoskeletal system, slow muscles and substantial neural delays. Most physical interaction tasks require force generation, and that can be influenced by body configuration and task stability. In this work we investigate the human limits of force generation with respect to these factors. We devised an experiment in which healthy individuals performed a maximum voluntary pushing task using a custom-designed gimbal apparatus which allowed us to change the ability to transmit torque at the wrist. The experiment was repeated in two different arm positions to assess the effect of limb configuration. The results showed a substantial decrease in maximum force with less wrist ability to generate stabilizing stiffness, and minimal difference in maximum pushing force between the two arm configurations. Our findings confirm our hypothesis that stiffness production appears to be a prominent factor limiting force exertion. The limits of human force exertion remain an open and challenging research question in human biomechanics and motor neuroscience. Here, the authors demonstrate that the ability to generate stabilizing stiffness during force exertion is a key factor limiting performance.

Ensuring symmetric stiffness in impedance-controlled robots is crucial for physically meaningful and stable interaction in contact-rich manipulation. Conventional approaches neglect the change of basis vectors in curved spaces, leading to an asymmetric joint-space stiffness matrix that violates passivity and conservation principles. In this work, we derive a physically consistent, symmetric joint-space stiffness formulation directly from the task-space stiffness matrix by explicitly incorporating Christoffel symbols. This correction resolves long-standing inconsistencies in stiffness modeling, ensuring energy conservation and stability. We validate our approach experimentally on a robotic system, demonstrating that omitting these correction terms results in significant asymmetric stiffness errors. Our findings bridge theoretical insights with practical control applications, offering a robust framework for stable and interpretable robotic interactions.

Approximately 50% of survivors of stroke experience lasting balance impairments that persist and that are often managed through compensatory, but suboptimal, strategies. Identifying neuromechanical control changes after stroke could enable more targeted and effective rehabilitation strategies. Computational modeling has begun to uncover balance control strategies in unimpaired adults, but efforts have been limited post-stroke. Here we show one of the first instances of a model of quiet stance that reveals distinct control strategies in post-stroke individuals compared to similarly-aged unimpaired participants. Quiet standing was modeled using a double-inverted pendulum with full-state feedback control. The controller parameters were fit to foot-ground force data collected from 12 post-stroke and 22 similarly-aged unimpaired participants. The best-fit models revealed a joint-torque-coordination pattern in the paretic limb of post-stroke participants that differed substantially from that of the unimpaired participants. The post-stroke participants' non-paretic limb also showed increased reliance on neural feedback, which may quantify compensatory effort for the altered coordination in the paretic limb. The results demonstrate that model-based analysis of foot-ground force behavior could eventually reveal clinically meaningful insights that are not captured by traditional assessments.

Learning-based methods excel at robot motion generation but remain limited in contact-rich physical interaction. Impedance control provides stable and safe contact behavior but requires task-specific tuning of stiffness and damping parameters. We present Diffusion-Based Impedance Learning, a framework that bridges these paradigms by combining generative modeling with energy-consistent impedance control. A Transformer-based Diffusion Model, conditioned via cross-attention on measured external wrenches, reconstructs simulated Zero-Force Trajectories (sZFTs) that represent contact-consistent equilibrium behavior. A SLERP-based quaternion noise scheduler preserves geometric consistency for rotations on the unit sphere. The reconstructed sZFT is used by an energy-based estimator to adapt impedance online through directional stiffness and damping modulation. Trained on parkour and robot-assisted therapy demonstrations collected via Apple Vision Pro teleoperation, the model achieves sub-millimeter positional and sub-degree rotational accuracy using only tens of thousands of samples. Deployed in realtime torque control on a KUKA LBR iiwa, the approach enables smooth obstacle traversal and generalizes to unseen tasks, achieving 100% success in multi-geometry peg-in-hole insertion.

In contrast to robots, humans can rapidly and elegantly modulate the impedance of their arms and hands during initial contact with objects. Anticipating collisions by setting mechanical impedance to counter near-instantaneous changes in force and displacement is one reason we excel at manipulating objects. We investigated the ability to set impedance in an object interaction task with rapid changes in force and displacement, like those encountered during manipulation in different directions. Subjects (n = 20) predictively co-activated antagonist muscles to adjust one component of the impedance - stiffness - to match the task demands before the movement began, irrespective of movement direction. Subjects adopted the minimal stiffness needed to complete the task, but when pushed to the most difficult condition, they were limited by their ability to produce high stiffness rather than large force. This robust and simple strategy ensured task success at the expense of energy efficiency. Our results confirm the ability of humans to predictively set and control mechanical impedance in task-relevant directions in anticipation of breaking contact. This offers the prospect that future investigations will find neural correlates of impedance, which in turn, could improve the ability of neuro-prosthetic limbs to interact with objects.

Despite a slow neuromuscular system, humans easily outperform modern robot technology, especially in physical contact tasks. How is this possible? Biological evidence indicates that motor control of biological systems is achieved by a modular organization of motor primitives, which are fundamental building blocks of motor behavior. Inspired by neuro-motor control research, the idea of using simpler building blocks has been successfully used in robotics. Nevertheless, a comprehensive formulation of modularity for robot control remains to be established. In this paper, we introduce a modular framework for robot control using motor primitives. We present two essential requirements to achieve modular robot control: independence of modules and closure of stability. We describe key control modules and demonstrate that a wide range of complex robotic behaviors can be generated from this small set of modules and their combinations. The presented modular control framework demonstrates several beneficial properties for robot control, including task-space control without solving Inverse Kinematics, addressing the problems of kinematic singularity and kinematic redundancy, and preserving passivity for contact and physical interactions. Further advantages include exploiting kinematic singularity to maintain high external load with low torque compensation, as well as controlling the robot beyond its end-effector, extending even to external objects. Both simulation and actual robot experiments are presented to validate the effectiveness of our modular framework. We conclude that modularity may be an effective constructive framework for achieving robotic behaviors comparable to human-level performance.

The analysis of human movements has highlighted the presence of stereotyped coordination patterns among the different joints of the human body. These patterns are commonly referred to as kinematic synergies. Synergies have been used to both elucidate the underlying neuromotor control strategies adopted by humans during coordinated motion and inform the design and control of assistive and rehabilitative devices such as prostheses and exoskeletons. A particularly thorny problem in the analysis of synergies is the comparison of the synergy postures i.e., the hyper-dimensional vectors containing the contribution of each analyzed feature (e.g., joint angles) to the considered synergies. Often, synergy postures are compared using cosine similarity, which is sensitive to the dimensionality of the input data and does not offer an intuitive understanding of the synergies' similarities and differences. In this study, we introduce a new geometric method, Geometric Configuration Similarity (GCS), specifically designed to compare kinematic synergy postures, with a particular emphasis on hand kinematic synergies. GCS provides a more intuitive geometric understanding of how these postures relate to one another. We demonstrate its advantages over cosine similarity through experimental and numerical results, offering the human motor control and rehabilitation robotics communities a new tool for analyzing kinematic hand synergies and improving the design and control of assistive systems.

Measuring static postural sway outside of the clinic could provide clinicians with long-term, continuous data on patient balance, offering a comprehensive view beyond infrequent in-clinic assessments. This paper presents a novel method to quantify balance ability through a regression algorithm that predicts postural sway velocity using only motion and force sensors. Data is acquired through sensors onboard an instrumented cane. The prediction algorithm's validity was demonstrated in a study of eight young unimpaired subjects and eight adults over 65. The subjects' balance was challenged with different stance widths and sight conditions while using an instrumented cane. In the younger subject cohort, balance was further challenged through an unstable platform. Together, these conditions allowed for variation of the tasks' difficulty levels and thus the range of measured sway velocity. Across subjects, sway velocity was demonstrated to be highly predictable (Younger Subjects $R^{2}=0.73$, Older Subjects $R^{2}= 0.47$) using just the sensors onboard the instrumented cane. In particular, hand motion was shown to be important in predicting sway velocity. We also demonstrated the use of data features to estimate Romberg quotients of the older participants, suggesting the method's potential to track proprioceptive function over time (Correlation $\mathbf{r}=0.82$). This method offers a promising approach to continuous patient monitoring and could provide a long-term, quantitative assessment of balance ability.

The search for an answer to Bernstein's degrees of freedom problem has propelled a large portion of research studies in human motor control over the past six decades. Different theories have been developed to explain how humans might use their incredibly complex neuro-musculo-skeletal system with astonishing ease. Among these theories, motor synergies appeared as one possible explanation. In this work, the authors investigate the nature and role of synergies and propose a theoretical framework, namely the "expansion hypothesis," to answer Bernstein's problem. The expansion hypothesis is articulated in three propositions: mechanical, developmental, and behavioral. Each proposition addresses a different question on the nature of synergies: i) How many synergies can humans have? ii) How do we learn and develop synergies? iii) How do we use synergies? An example numerical simulation is presented and analyzed to clarify the hypothesis propositions. The expansion hypothesis is contextualized with respect to the existing literature on motor synergies both in healthy and impaired individuals, as well as other prominent theories in human motor control and development. The expansion hypothesis provides a framework to better comprehend and explain the nature, use, and evolution of human motor skills.

Abstract The dynamics of any mechanical system can be described in terms of forces and motions. The interaction of these terms is often captured in the metric of mechanical impedance – a generalization of stiffness – used to describe how a mechanical system resists the application of force. The ability to adaptively change impedance is advantageous when encountering variable environmental conditions. Changing impedance in robotic systems is limited, whereas humans can rapidly and elegantly adapt the impedance of their limbs, especially during initial contact with objects. This is especially true for movements of our arms and hands. Multiple studies have examined the arm’s response to perturbation with the idea of impedance as a reactive component. In this study, we investigate the ability of humans to predictively set their arm impedance in a contact breaking task to perform fast movements to target positions in different directions. Our findings show that subjects (n=20) predictively co-activate antagonist muscles to primarily adjust one component of the arm’s impedance – stiffness – to match different task constraints before the movement begins irrespective of movement direction. Interestingly, the subjects’ performance was limited by the task-dependent stiffness rather than the required force and they tended to generate minimal stiffness to perform the task. With this robust strategy, task success is optimized at the expense of energy efficiency. This type of control is essential for the uniquely human ability to interact with objects.

In this work we tackle the question of how to analyze and objectively quantify the complexity of a manipulation task. The study investigates the kinematic behavior of the hand joints in three different manipulation tasks of growing complexity: reaching-to-grasp, tool use and piano playing. The collected data were processed to extract the kinematic synergies of the hand by means of singular value decomposition. A novel, unbiased metric to determine hand manipulation complexity was based on the cumulative variance accounted for. This Variance-Accounted-For Complexity Index (VAF-CI) reliably distinguished between different manipulation tasks. Moreover, an unsupervised learning method (k-means clustering) was able to use the index to accurately identify the 3 distinct manipulation tasks. These results may be leveraged to improve the control of biomimetic dexterous robots during manipulation tasks.

This paper presents a modular framework for motion planning using movement primitives. Central to the approach is Contraction Theory, a modular stability tool for nonlinear dynamical systems. The approach extends prior methods by achieving parallel and sequential combinations of both discrete and rhythmic movements, while enabling independent modulation of each movement. This modular framework enables a divide-and-conquer strategy to simplify the programming of complex robot motion planning. Simulation examples illustrate the flexibility and versatility of the framework, highlighting its potential to address diverse challenges in robot motion planning.

This paper investigates robotic peg-in-hole assembly using the Elementary Dynamic Actions (EDA) framework, which models contact-rich tasks through a combination of submovements, oscillations, and mechanical impedance. Rather than focusing on a single optimal parameter set, we analyze the distribution and structure of multiple successful impedance solutions, revealing patterns that guide impedance selection in contactrich robotic manipulation. Experiments with a real robot and four different peg types demonstrate the presence of task-specific and generalized assembly strategies, identified through K-means Clustering. Principal Component Analysis (PCA) is used to represent these findings, highlighting patterns in successful impedance selections. Additionally, a neural-network-based success predictor accurately estimates feasible impedance parameters, reducing the need for extensive trial-and-error tuning. By providing publicly available code, CAD files, and a trained model, this work enhances the accessibility of impedance control and offers a structured approach to programming robotic assembly tasks, particularly for less-experienced users.

This study explored balance control in humans during a challenging task using the novel intersection point analysis, based on foot-ground force direction and point of application. Experimental data of subjects standing on a narrow beam in tandem stance were compared with modeling results of a double-inverted pendulum. The analysis showed that individuals minimized effort by adjusting ankle and hip torques, shedding light on the interplay of biomechanics and neural control in maintaining balance.

Deriving a robot’s equations of motion typically requires placing multiple coordinate frames, commonly using the Denavit-Hartenberg convention to express the kinematic and dynamic relationships between segments. This paper presents an alternative using the differential geometric method of Exponential Maps, which reduces the number of coordinate frame choices to two. The traditional and differential geometric methods are compared, and the conceptual and practical differences are detailed. The open-source software, $\operatorname{Exp}[\text { licit }]^{\mathrm{TM}}$, based on the differential geometric method, is introduced. It is intended for use by researchers and engineers with basic knowledge of geometry and robotics and aims to serve as a supportive resource during the study of differential geometric approaches. Code snippets and an example application are provided to demonstrate the benefits of the differential geometric method and assist users to get started with the software.

Motor primitives are fundamental building blocks of a controller which enable dynamic robot behavior with minimal high-level intervention. By treating motor primitives as basic “modules,” different modules can be sequenced or superimposed to generate a rich repertoire of motor behavior. In robotics, two distinct approaches have been proposed: Dynamic Movement Primitives (DMPs) and Elementary Dynamic Actions (EDAs). While both approaches instantiate similar ideas, significant differences also exist. This paper attempts to clarify the distinction and provide a unifying view by delineating the similarities and differences between DMPs and EDAs. We provide nine robot control examples, including sequencing or superimposing movements, managing kinematic redundancy and singularity, control of both position and orientation of the robot’s end-effector, obstacle avoidance, and managing physical interaction. We show that the two approaches clearly diverge in their implementation. We also provide a real-robot demonstration to show how DMPs and EDAs can be combined to get the best of both approaches. With this detailed comparison, we enable researchers to make informed decisions to select the most suitable approach for specific robot tasks and applications.

<title>Abstract</title> The search for an answer to Bernstein’s degrees of freedom problem has propelled a large portion of research studies in human motor control over the past six decades. Different theories have been developed to explain how humans might use their incredibly complex neuro-musculo-skeletal system with astonishing ease. Among these theories, motor synergies appeared as one possible explanation. In this work, the authors investigate the nature and role of synergies and propose a new theoretical framework, namely the “expansion hypothesis”, to answer Bernstein’s problem. The expansion hypothesis is articulated in three propositions: mechanical, developmental, and behavioral. Each proposition addresses a different question on the nature of synergies: (i) How many synergies can humans have? (ii) How do we learn and develop synergies? (iii) How do we use synergies? An example numerical simulation is presented and analyzed to clarify the hypothesis propositions. The expansion hypothesis is contextualized with respect to the existing literature on motor synergies both in healthy and impaired individuals, as well as other prominent theories in human motor control. The expansion hypothesis provides a novel and testable framework to better comprehend and explain the nature, use and evolution of human motor skills.

ABSTRACT Aging can cause the decline of balance ability, which can lead to increased falls and decreased mobility. This work aimed to discern differences in balance control strategies between healthy older and younger adults. Foot force data of 38 older and 65 younger participants (older and younger than 60 years, respectively) were analyzed. To first determine whether the two groups exhibited any differences, this study incorporated the orientation of the foot-ground interaction force in addition to its point of application. Specifically, the frequency-dependence of the “intersection point” of the lines of actions of the foot-ground interaction force were evaluated. Results demonstrated that, like the mean center-of-pressure speed, a traditionally-employed measure, the intersection-point analysis could distinguish between the two participant groups. Then, to further explore age-specific control strategies, simulations of standing balance were conducted. An optimal controller stabilized a double-inverted-pendulum model with torque-actuated ankle and hip joints corrupted with white noise. The experimental data were compared to the simulation results to identify the controller parameters that best described the human data. Older participants showed significantly more use of the ankle than hip compared to younger participants. Best-fit controller gains suggested diminished intrinsic muscle stiffness in older adults, indicative of muscle strength loss, that was likely compensated by increased neural feedback. These results underscore the advantages of the intersection-point analysis to quantify shifts in interjoint control strategies with age, thus highlighting its potential to be used as a balance assessment tool in research and clinical settings. NEW &amp; NOTEWORTHY Age groups were distinguished by analyzing foot-ground force data during quiet standing in older and younger adults to calculate the foot-force vector intersection point that emerges across frequency bands. Modeling balance and comparing the simulations’ outcomes with experimental results suggested that older adults increased reliance on neural feedback, possibly compensating for muscle strength deficiency. This novel analysis also quantified controllers for each participant, highlighting its potential to be implemented as a balance assessment tool.

The search for an answer to Bernstein's degrees of freedom problem has propelled a large portion of research studies in human motor control over the past six decades. Different theories have been developed to explain how humans might use their incredibly complex neuro-musculo-skeletal system with astonishing ease. Among these theories, motor synergies appeared as one possible explanation. In this work, the authors investigate the nature and role of synergies and propose a new theoretical framework, namely the 'expansion hypothesis', to answer Bernstein's problem. The expansion hypothesis is articulated in three propositions: mechanical, developmental, and behavioral. Each proposition addresses a different question on the nature of synergies: (i) How many synergies can humans have? (ii) How do we learn and develop synergies? (iii) How do we use synergies? An example numerical simulation is presented and analyzed to clarify the hypothesis propositions. The expansion hypothesis is contextualized with respect to the existing literature on motor synergies both in healthy and impaired individuals, as well as other prominent theories in human motor control and development. The expansion hypothesis provides a novel framework to better comprehend and explain the nature, use and evolution of human motor skills.

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.

ABSTRACT Despite the abundance of studies on the control of standing balance, insights about the roles of biomechanics and neural control have been limited. Previous work introduced an analysis combining the direction and orientation of ground reaction forces. The “intersection point” of the lines of actions of these forces exhibited a consistent pattern across healthy, young subjects when computed for different frequency components of the center of pressure signal. To investigate the control strategy of quiet stance, we applied this intersection point analysis to experimental data of 15 healthy, young subjects balancing in tandem stance on a narrow beam and on the ground. Data from the sagittal and frontal planes were analyzed separately. The task was modeled as a double-inverted pendulum controlled by an optimal controller with torque-actuated ankle and hip joints and additive white noise. To test our prediction that the controller that minimized overall joint effort would yield the best fit across the tested conditions and planes of analyses, experimental results were compared to simulation outcomes. The controller that minimized overall effort produced the best fit in both balance conditions and planes of analyses. For some conditions, the relative penalty on the hip and ankle joints varied in a way relevant to the balance condition or to the plane of analysis. These results suggest that unimpaired quiet balance in a challenging environment can be best described by a controller that maintains minimal effort through the adjustment of relative ankle and hip joint torques. NEW &amp; NOTEWORTHY This study explored balance control in humans during a challenging task using the novel intersection point analysis, based on ground reaction force direction and point of application. Experimental data of subjects standing on a narrow beam in tandem stance were compared with modeling results of a double-inverted pendulum. The analysis showed that individuals minimized effort by adjusting ankle and hip torques, shedding light on the interplay of biomechanics and neural control in maintaining balance.

Abstract 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.

This folder contains measurements during the task of planar crank turning. This data was collected as part of Joseph Doeringer's PhD thesis[Link]. This data set contains measurements of crank positions, crank velocities, forces, moments, and EMG. Further documentation is included in the ReadMe.txt file. The methods sections of the cited papers explicitly detail the experimental protocol. This data set was presented in the following journal papers: James Hermus, Joseph Doeringer, Dagmar Sternad, and Neville Hogan, "Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions" Journal of Neurophysiology 2020 123:5, 1870-1885 [Link] James R Hermus, Joseph Doeringer, Dagmar Sternad, and Neville Hogan, "Dynamic Primitives in Constrained Action: Systematic Changes in the Zero-Force Trajectory" Currently accepted to the Journal of Neurophysiology [Link] The code used for analysis in the paper "Dynamic Primitives in Constrained Action" is also archived on Zenodo [Link] and available on Github [Link]. This data set was also presented in a conference paper: J. Hermus, D. Sternad and N. Hogan, "Evidence for Dynamic Primitives in a Constrained Motion Task," 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA, 2020, pp. 551-556. [Link]

This folder contains measurements during the task of planar crank turning. This data was collected as part of Joseph Doeringer's PhD thesis[Link]. This data set contains measurements of crank positions, crank velocities, forces, moments, and EMG. Further documentation is included in the ReadMe.txt file. The methods sections of the cited papers explicitly detail the experimental protocol. This data set was presented in the following journal papers: James Hermus, Joseph Doeringer, Dagmar Sternad, and Neville Hogan, "Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions" Journal of Neurophysiology 2020 123:5, 1870-1885 [Link] James R Hermus, Joseph Doeringer, Dagmar Sternad, and Neville Hogan, "Dynamic Primitives in Constrained Action: Systematic Changes in the Zero-Force Trajectory" Currently accepted to the Journal of Neurophysiology [Link] The code used for analysis in the paper "Dynamic Primitives in Constrained Action" is also archived on Zenodo [Link] and available on Github [Link]. This data set was also presented in a conference paper: J. Hermus, D. Sternad and N. Hogan, "Evidence for Dynamic Primitives in a Constrained Motion Task," 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA, 2020, pp. 551-556. [Link]

Motor primitives are fundamental building blocks of a controller which enable dynamic robot behavior with minimal high-level intervention. By treating motor primitives as basic "modules," different modules can be sequenced or superimposed to generate a rich repertoire of motor behavior. In robotics, two distinct approaches have been proposed: Dynamic Movement Primitives (DMPs) and Elementary Dynamic Actions (EDAs). While both approaches instantiate similar ideas, significant differences also exist. This paper attempts to clarify the distinction and provide a unifying view by delineating the similarities and differences between DMPs and EDAs. We provide eight robot control examples, including sequencing or superimposing movements, managing kinematic redundancy and singularity, obstacle avoidance, and managing physical interaction. We show that the two approaches clearly diverge in their implementation. We also discuss how DMPs and EDAs might be combined to get the best of both approaches. With this detailed comparison, we enable researchers to make informed decisions to select the most suitable approach for specific robot tasks and applications.

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.

In this paper, a kinematically modular approach to robot control is presented. The method involves structures called Elementary Dynamic Actions and a network model combining these elements. With this control framework, a rich repertoire of movements can be generated by combination of basic modules. The problems of solving inverse kinematics, managing kinematic singularity and kinematic redundancy are avoided. The modular approach is robust against contact and physical interaction, which makes it particularly effective for contact-rich manipulation. Each kinematic module can be learned by Imitation Learning, thereby resulting in a modular learning strategy for robot control. The theoretical foundations and their real robot implementation are presented. Using a KUKA LBR iiwa14 robot, three tasks were considered: (1) generating a sequence of discrete movements, (2) generating a combination of discrete and rhythmic movements, and (3) a drawing and erasing task. The results obtained indicate that this modular approach has the potential to simplify the generation of a diverse range of robot actions.

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.

Abstract There is an urgent need to provide ways to help a fast-growing older adult population to maintain daily mobility. A great deal of work exists in medical devices and robotics to generate effective assistive solutions, yet at the same time, limits have been observed in the adoption of such systems. In this paper we explore possible factors in adoption from a user-centered design perspective. We investigated user needs surrounding the act of standing up from a seated position and older users’ attitudes toward assistive device prototypes. Older adults completed a standard timed-up-and-go mobility assessment, rated their own difficulty standing, participated in interviews, and shared responses to “look-and-feel” prototypes, all in an effort to uncover latent needs. A licensed physical therapist rated videos of the subjects while standing up and the two ratings were compared. While the physical therapist’s rating of difficulty increased as subjects’ performance on the clinical mobility assessment worsened, subjects’ self-ratings did not significantly correlate with mobility performance, even when timed-up-and-go performance indicated a risk of falling. Subjects expressed preferences for potential assistive devices that were more discreet, lightweight, and flexible over devices that were bulkier, heavier, and rigid. In general, subjects’ attitudes toward assistive devices for their own sit-to-stand use were similar regardless of their demonstrated need. The results highlight the challenges designers may face when creating products for older adult users and underline the importance of a user-centered design process. Implications for assistive technology design are discussed.

Abstract 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.