Biology subjects

Burdet, E.

Publications and source records attributed to Burdet, E..

13 recordsLinked to original sources

bioRxiv2026

Out-of-sight partners: autonomous wearable supernumerary limbs for dynamic co-manipulation

Supernumerary robotic limbs (SLs) could extend human motor capabilities beyond the natural body, yet most demonstrations have been limited to simple, quasi-static tasks or direct teleoperation under visual control. Whether humans can safely and intuitively collaborate with autonomous SLs during complex, dynamic tasks remains unknown. We studied this question in a demanding assembly task requiring coordinated transport and handovers of a long object, which could not be completed by a single person. We developed a reconfigurable backpack platform mounting up to four high-payload robotic arms, together with safety-aware motion planning that generates human-compatible trajectories from task states without training data. Despite the SLs operating out of sight, participants adapted quickly, completed the task reliably, and reported low cognitive demand alongside high perceived safety and predictability. Kinematic and force analyses showed that control design governed both movement fluency and whole-body stability: faster limb motions yielded smoother interactions and shorter completion times, and a mirrored coordination strategy--in which one robotic limb counterbalanced the other--further shortened duration and reduced variability in left-right ground reaction force differences during co-manipulation. These results show that humans can integrate autonomous wearable limbs as out-of-sight partners in dynamic whole-body collaboration, and delineate behavioral and control principles underpinning fluency, stability, and usability in human augmentation.

bioengineering↗

bioRxiv2026

Explainable Decoding of Sensorimotor Communication in Joint Object Manipulation

Humans often collaborate under asymmetric information, for example when two people carry a table and only one knows the destination. They coordinate without speech using cues from movement kinematics, interaction forces, and object states. Characterizing this sensorimotor communication is difficult because these signals both execute the task and convey information, whose meaning is context-dependent. Here, we investigated a virtual table-carrying task where one partner knew the target while the other inferred it from visuo-haptic feedback. Participants flexibly adapted kinematic and haptic cues across contexts to convey intention. We introduce an explainable machine-learning framework that decodes intent from ongoing multimodal signals and quantifies where individual features are informative. Incorporating the decoded signals into a drift-diffusion model accurately predicted the uninformed partner's target choices and decision times. Together, our framework explains how humans communicate through action and offers principles for collaborative robots to infer and express intent through physical interaction.

neuroscience↗

bioRxiv2026

Information-dependent eye-hand coordination emerges from active vision

In daily activities, humans rely on visual information to plan hand movements, making the extraction of task-relevant information through eye gaze a key aspect of motor control. Behavioral studies have revealed characteristic saccade-pursuit patterns, likely governed by shared neural circuits, which enable an efficient reduction of task-related uncertainty. However, a unifying computational principle explaining the emergence of these patterns in continuous tasks such as reading or driving is still lacking. Here we propose a dual stochastic model predictive control formulation of active vision, in which eye movements are continuously controlled to minimize task-relevant uncertainty and build an internal model used for hand movement planning. Through experiments manipulating the amount, density, and difficulty of future visual information, we show how eye movement patterns adapt to the information context while maintaining an invariant extraction horizon. A saccade-pursuit pattern naturally emerges from the model, which accurately predicts both eye and hand movement features observed in experiments. These results provide a principled framework for understanding the continuous regulation of human eye movements and open new perspectives for applications in robotic assistance and active perception.

neuroscience↗

bioRxiv2025

How individual vigor shapes human-human physical interaction

The speed of voluntary movements varies systematically, with some individuals moving consistently faster than others across different actions. These variations, conceptualized as vigor, reflect a time-effort-accuracy tradeoff in motor planning. How do two mechanically coupled partners with different individual vigors collaborate, e.g. to move a table together? Here, we show that such dyads coordinate goal-directed movements with minimal interaction force, exhibiting a dyadic vigor with similar characteristics as individual vigor. The emerging dyadic motor plan is strongly influenced by the slower partner, whose vigor predicts dyadic vigor, with effects lasting beyond practice. Computational modeling with stochastic optimal control reveals the critical role of partners movement timings uncertainty and vigor in shaping coordination, allowing to predict dyadic movements from individual behavior across diverse conditions. These findings shed light on the mechanisms underlying human collaboration, and may be used in applications ranging from physical training and rehabilitation to collaborative robotics for manufacturing.

neuroscience↗

bioRxiv2025

Intermittent movement control emerges from information-based planning

Mammalian motor control is inherently discrete, with movement corrections occurring at rates determined by task demands and the quality of sensory information. While several models have been proposed to explain this discreteness, it remains unclear when a new movement should be initiated and how long it should last. To address this gap, we introduce the Information Predictive Control (IPC) framework, which combines model predictive control with information theory. IPC triggers corrections only when unexpected deviations arise and when corrective actions are likely to succeed. By quantifying "surprise" relative to predicted internal and external states, IPC generates successful movements while robustly integrating sensorimotor noise, task constraints, and target variability. Simulations show that IPC reproduces human-like behavior in discrete reaching, continuous target tracking, and adaptive planning under uncertainty, while dynamically adjusting the planning horizon in complex, unpredictable environments.

neuroscience↗

bioRxiv2025

Model predictive game control for personalized and targeted interactive assistance

Contact robots are increasingly used to assist humans in physical training and manufacturing tasks. However, the effectiveness of current systems is limited as their control focuses on the system performance without explicitly considering the upcoming human users control. Here we present a differential game-based controller for contact robots that ensures optimal interaction with the human user by predicting their control during movement while considering their inherently finite planning horizon. Using this model-predictive game (MPG) controller, we systematically investigated human-robot co-adaptation in experiments, demonstrating that: (a) interaction with MPG remains stable for all participants while effectively reducing human effort; (b) the robot adapts to human behavior, identifying and characterizing individual motor control strategies that remain consistent over time and may be used as control biomarkers; (c) the human adapts to the robots behavior, and their interaction behavior can be modulated through an assistance meta-parameter. These findings indicate that humans can understand and adapt to a partners control strategy during movement, thereby exhibiting behavior consistent with game theory principles. Furthermore, the ability of the assistance meta-parameter to guide human users toward specific interaction behaviors can be used to develop versatile robot-assisted learning systems for physical training and rehabilitation.

bioengineering↗

bioRxiv2025

A modular architecture for trial-by-trial learning of redundant muscle activity patterns in novel sensorimotor tasks

The coordination of the multiple degrees-of-freedom of the human body may be simplified by muscle synergies, motor modules which can be flexibly combined to achieve various goals. Studies investigating adaptation to novel relationships between muscle activity and task outcomes found that altering the recruitment of such modules is faster than the learning of their structures de novo. However, how learning new synergy recruitments or new synergy structures may occur remains unclear. While trial-by-trial learning of novel sensorimotor tasks has been successfully modeled at the level of task variables, few models accounted for the redundancy of the motor system, particularly at the muscular level. However, these models either did not consider a modular architecture of the motor system, or assumed a priori knowledge of the sensorimotor task. Here, we present a computational model for the generation of redundant muscle activity where explicitly defined modules, implemented as spatial muscle synergies, can be updated together with their recruitment coefficients through an error-based learning process dependent on a forward model of the sensorimotor task, which is not assumed to be known a priori. Our model can qualitatively reproduce the experimental observations of slower learning and larger changes in the structure of the muscle activity under sensorimotor tasks that require the learning of novel patterns of muscle activity, providing further insights into the modular organization of the human motor system. Author summaryIt has been proposed that muscles are recruited in modules, called muscle synergies, rather than on a muscle-by-muscle basis. This modular organization was shown to affect the learning of novel tasks, where simulated remapping of the forces generated by the muscles ("virtual surgeries") that make the synergies ineffective are more difficult to learn. Previous models of trial-by-trial learning in a modular architecture have assumed prior knowledge of the motor task (in particular, how to modify the muscles / synergies recruitment given an error), which might not be the case for tasks in which novel patterns of muscle activity are required. In contrast, models which assumed no prior knowledge of the task have not investigated the role of modularity. Here, we propose a computational model of the trial-by-trial adaptation of muscle synergies, their recruitment, and the concurrent learning of the internal model of the musculoskeletal system responsible for error correction. Our results show that this model replicates experimental observations of slower learning and larger changes in the structure of the muscle activity in sensorimotor tasks that require the learning of novel patterns of muscle activity, providing insight into learning-related changes of muscle activity in novel sensorimotor tasks.

neuroscience↗

bioRxiv2024

Effects of Uni- and Bidirectional Interaction During Dyadic Ankle and Wrist Tracking

Haptic human-robot-human interaction allows users to feel and respond to one anothers forces while interfacing with separate robotic devices, providing customizable infrastructure for studying physical interaction during motor tasks (i.e., physical rehabilitation). For both upper- and lower-limb tasks, previous work has shown that virtual interactions with a partner can improve motor performance and enhance individual learning. However, whether the mechanism of these improvements generalizes across different human systems is an open question. In this work, we investigate the effects of haptic interaction between healthy individuals during a trajectory tracking task involving single-joint movements at the wrist and ankle. We compare tracking performance and muscle activation during haptic conditions where pairs of participants were uni- and bidirectionally connected, in order to investigate the contribution of real-time responses from a partner during the interaction. Findings indicate similar improvements in tracking performance during the bidirectional interaction for both the wrist and ankle, despite significant differences in how individuals modulated co-contraction. For each joint, bidirectional and unidirectional interaction resulted in similar improvements for the worse partner in the dyad. For the better partner, bidirectional interaction outperformed unidirectional interaction, likely due to changes in movement planning that were not observed in the unidirectional condition. While these results suggest that unidirectional interaction is sufficient for error correction of less skilled individuals during simple motor tasks, they also highlight the mutual benefits of bidirectional interaction which are consistent across the upper and lower limbs.

bioengineering↗

bioRxiv2024

Co-Contraction Embodies Uncertainty: An Optimal Feedforward Strategy for Robust Motor Control

Despite our environment is often uncertain, we generally manage to generate stable motor behaviors. While reactive control plays a major role in this achievement, proactive control is critical to cope with the substantial noise and delays that affect neuromusculoskeletal systems. In particular, muscle co-contraction is exploited to robustify feedforward motor commands against internal sensorimotor noise as was revealed by stochastic optimal open-loop control modeling. Here, we extend this framework to neuromusculoskeletal systems subjected to random disturbances originating from the environment. The analytical derivation and numerical simulations predict a singular relationship between the degree of uncertainty in the task at hand and the optimal level of anticipatory co-contraction. This prediction is confirmed through a single-joint pointing task experiment where an external torque is applied to the wrist near the end of the reaching movement with varying probabilities across blocks of trials. We conclude that uncertainty calls for impedance control via proactive muscle co-contraction to stabilize behaviors when reactive control is insufficient for task success. Author summaryThis work presents a computational framework for predicting how humans modulate muscle co-contraction to cope with uncertainties of different origins. In our neuromusculoskeletal system, uncertainties have both internal (sensorimotor noise) and external (environmental randomness) origins. The present study focuses on the latter type of uncertainty, which had not been dealt with systematically previously despite its importance in everyday life. Therefore, we thoroughly investigated how random disturbances occurring with some probability in a motor task shape the feedforward control of mechanical impedance through muscle co-contraction. Here we provide theoretical, numerical and experimental evidence that the optimal level of co-contraction steeply increases with the uncertainty of our environment. These findings show that muscle co-contraction embodies uncertainty and optimally mitigates its consequences on task execution when feedback control is insufficient due to sensory noise and delays.

neuroscience↗

bioRxiv2024

During haptic communication, the central nervous system compensates distinctly for delay and noise

Connected humans have been previously shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is important to understand the impact that network features such as lag and noise may have on human behaviour. In this paper, we investigated the interaction with a human-like robot controller that provides similar haptic communication behaviour as human-human interaction and examined the influence and compensation mechanisms for delay and noise on haptic communication. The results of our experiments show that participants can distinguish between noise and delay, and make use of compensation mechanisms to preserve performance in both cases. However, while noise is compensated for by increasing co-contraction, delay compensation could not be explained by this strategy. Instead, computational modelling suggested that a feed-forward prediction mechanism is used to compensate for the temporal delay and yield an efficient haptic communication. Author summaryIncreasingly humans are making use of networks and robots to coordinate haptic interactions through teleoperation. However, with networks comes delays and noise that can change both the force that is transmitted and how we perceive that force. The haptic communication involved in joint actions, such as moving a piano or performing a pair spin, has been shown to improve performance, but how does delay affect this behaviour? We tested how participants tracked a moving target with their right hand when connected to a human-like robotic partner, when perturbed by delay or noise. Through a comparison between noise and delay perturbation in experimental performance and in simulation with a computational model, we found that participants could from small values of perturbation identify if the perturbation was from delay or noise and that they adopted different compensation strategies in each case.

neuroscience↗

bioRxiv2023

Control limitations in the null-space of the wrist muscle system

The redundancy present within the musculoskeletal system may offer a non-invasive source of signals for movement augmen tation, where the muscle-to-force null-space could be controlled simultaneously to the natural limbs. Here, we investigated the viability of extracting movement augmentation control signals from the muscles of the wrist complex. Our study assessed i) if controlled variation of the muscle activation patterns in the wrist joints null-space is possible; and ii) whether force and null-space targets could be reached simultaneously. During the null-space target reaching condition, participants used EMG-to-force null-space muscle activation to move their cursor towards a displayed target while minimising the exerted force as visualised through the cursors size. Initial targets were positioned to require natural co-contraction in the null-space and if participants showed a consistent ability to reach for their current target, they would rotate 5{degrees} incrementally to generate muscle activation patterns further away from their natural co-contraction. In contrast, during the concurrent target reaching condition participants were required to match a target position and size, where their cursor position was instead controlled by their exerted flexion-extension and radial-ulnar deviation, while its size was changed by their natural co-contraction magnitude. The results collected from 10 participants suggest that while there was variation in each participants co-contraction behaviour, most did not possess the ability to control this variation for muscle-to-force null-space reaching. In contrast, participants did show a direction and target size dependent ability to vary isometric force and co-contraction activity concurrently. Our results show the limitations of using null-space activity in joints with a low level of redundancy.

neuroscience↗

bioRxiv2022

A model predictive control strategy to regulate movements and interactions

Humans are adept at moving the arm to interact with objects and surfaces. The brain is thought to regulate motion and interactions using two different controllers, one specialized for movements and the other for force regulation. However, it remains unclear whether different control mechanisms are necessary. Here we show that the brain can employ a single high-level control strategy for both movement and interaction control. The Model Predictive Control (MPC) strategy introduced in this paper uses an internal model of the environment to plan the arms muscle activity whilst updating its predictions using periodic feedback. Computer simulations demonstrate MPCs ability to produce human-like movements and after-effects in free and force field environments. It can simultaneously regulate both force and stiffness during interactions, and can accomplish motor tasks demanding transitions between motion and interaction control. Model Predictive Control promises to be an important tool to test ideas of motor control as it can handle nonlinear dynamics with changing environments and goals without having to specify the movement duration.

neuroscience↗

bioRxiv2021

The control and training of single motor units in isometric tasks are constrained by a common synaptic input signal

Recent developments in neural interfaces enable the real-time and non-invasive tracking of motor neuron spiking activity. Such novel interfaces provide a promising basis for human motor augmentation by extracting potential high-dimensional control signals directly from the human nervous system. However, it is unclear how flexibly humans can control the activity of individual motor neurones to effectively increase the number of degrees-of-freedom available to coordinate multiple effectors simultaneously. Here, we provided human subjects (N=7) with real-time feedback on the discharge patterns of pairs of motor units (MUs) innervating a single muscle (tibialis anterior) and encouraged them to independently control the MUs by tracking targets in a 2D space. Subjects learned control strategies to achieve the target-tracking task for various combinations of MUs. These strategies rarely corresponded to a volitional control of independent input signals to individual MUs. Conversely, MU activation was consistent with a common input to the MU pair, while individual activation of the MUs in the pair was predominantly achieved by alterations in de-recruitment order that could be explained with history-dependent changes in motor neuron excitability. These results suggest that flexible MU control based on independent synaptic inputs to single MUs is not a simple to learn control strategy.

neuroscience↗