Powered ankle–foot prostheses offer the potential to emulate natural locomotion dynamics, thereby addressing the issues related to uneven gait and insufficient propulsion typically experienced by individuals with lower limb amputation wearing a passive prosthetic device. Despite significant progress, existing powered prostheses are often hindered by their substantial build height, bulky design, excessive weight, and noise level, limiting their widespread adoption. This work presents ELSA (Efficient and Lightweight Spring Ankle), a lightweight (1.15 kg) and compact (11 cm high) powered ankle–foot prosthesis fitting within the volume of a shoe and capable of providing a net positive mechanical energy over the gait cycle. This level of integration is achieved through an innovative arrangement of a spring and actuator mechanisms operating in synergy. This hybrid architecture offers users the choice to walk actively, with propulsive energy assistance; regeneratively, potentially allowing for energy harvesting to recharge the device battery; or completely turnedoff(passive). This prototype has been validated during benchtop experiments and through trials involving four amputated participants. These tests encompassed various scenarios, including treadmill walking and everyday ambulation tasks. In addition, a sensitivity analysis was conducted to assess how different control parameters impacted the provided mechanical energy and resulting gait performance.

Lower-limb exoskeletons are wearable devices whose main purposes are human rehabilitation and bilateral locomotion assistance. In particular, there is a growing interest for their use to symmetrize the gait of hemiparetic patients. This often consists in using the kinematics of the less affected side as a reference for the most affected one. In this work, we followed this approach to design a symmetrization algorithm using the formalism of motor primitives, i.e. a low-dimensional set of signals that provide the desired assistance through their combination. The amount of variables to be stored in memory is thus intrinsically limited, and this framework is particularly adapted to include other modes of assistance and/or transitions between locomotion tasks. In this paper, we report the preliminary validation of this newly developed algorithm with a hip exoskeleton and a single participant replicating hemiparetic walking. Results show that the algorithm effectively managed to reduce both temporal and spatial gait asymmetry.

Parkinson's disease is a severe neurodegenerative disorder that affects sensorimotor control. In particular, several gait impairments are reported, including a decrease of long-range autocorrelations in stride duration time series. This complex statistics is potentially a biomarker of the risk of falling. This paper aims at developing model-based predictions about the loss of long-range autocorrelations in the gait of Parkinsonian patients, and how these autocorrelations can be restored by an oscillator-based walking assistance. Using a Super Central Pattern Generator model coupled with an adaptive oscillator, we show that this type of assistance has the potential to improve long-range autocorrelations in time series of Parkinsonian walkers. This requires however to tune the adaptive oscillator with slow learning gains, raising challenges for porting this method to an actual device.

Lower-limb exoskeletons are robotic devices that can provide assistance to human locomotion. Since they are expected to be used in ecological environments, their control strategy should handle different kinds of daily-life situations. Taking inspiration from the human neuromuscular system - and particularly from the socalled motor primitives - may help in adapting the type of delivered assistance to different locomotion tasks. In this work, we validated the combination of simplified primitives and a musculoskeletal model for assisting healthy subjects with a hip exoskeleton. This framework showed adaptation to the user's gait for different slope inclinations, although its effects on the subject's speed and their perceived effort showed no significant improvement compared to wearing the device in transparent mode.

Vision-based systems for terrain detection are ubiquitous in mobile robotics, while such systems recently emerged for locomotion assistance of disabled people. For instance, wearable devices embedding vision sensors can assist people in navigation; or guide lower-limb prosthesis or exoskeleton controller to retrieve gait patterns being adapted to the executed task (overground walking, stairs, slopes, etc.). In this research, we present a vision-based algorithm achieving the detection of flat ground, steps, and ramps, using a depth camera. The raw point cloud data obtained from the depth camera passes through multiple processing steps to extract environmental features, and then achieves terrain detection by feeding these features into a classification tree. The camera was mounted on a custom-made wearable tool that can be placed on the chest of human users. This contribution reports a pilot validation study with 6 healthy subjects moving in an indoor environment containing a rich set of different types of terrains. Our method can predict the locomotion modes up to three steps in front of the user. Moreover, it is able to perform terrain detection even if the path is partially occluded by another walker. The method accuracy with a cleared path was found to be above 90% for all locomotion modes.

To increase the autonomy of humanoid robots, the visual perception must support the efficient collection and interpretation of visual scene cues by providing task-dependent information. Active vision systems allow to extend the observable workspace by employing active gaze control, i.e. by shifting the gaze to relevant areas in the scene. When moving the eyes, stabilization of the camera images is crucial for successful task execution. In this paper, we present an active vision system for task-oriented selection of view directions and gaze stabilization to enable a humanoid robot to robustly perform vision-based tasks. We investigate the interaction between a gaze stabilization controller and view planning to select the next best view direction based on saliency maps which encode task-relevant information. We demonstrate the performance of the systems in a real world scenario, in which a humanoid robot is performing vision-based grasping while moving, a task that would not be possible without the combination of view selection and gaze stabilization.

Controllers based on neuromuscular models hold the promise of energy-efficient and human-like walkers. However, most of them rely on optimizations or cumbersome hand-tuning to find controller parameters which, in turn, are usually working for a specific gait or forward speed only. Consequently, designing neuromuscular controllers for a large variety of gaits is usually challenging and highly sensitive. In this contribution, we propose a neuromuscular controller combining reflexes and a central pattern generator able to generate gaits across a large range of speeds, within a single optimization. Applying this controller to the model of COMAN, a 95 cm tall humanoid robot, we were able to get energy-efficient gaits ranging from 0.4 m/s to 0.9 m/s. This covers normal human walking speeds once scaled to the robot height. In the proposed controller, the robot speed could be continuously commanded within this range by changing three high-level parameters as linear functions of the target speed. This allowed large speed transitions with no additional tuning. By combining reflexes and a central pattern generator, this approach can also predict when the next strike will occur and modulate the step length to step over a hole.

Recent studies showed that Continuously Variable Transmissions (CVT) and Infinitely Variable Transmissions (IVT) can considerably improve the locomotion efficiency in legged robot. A CVT is a transmission whose ratio can be continuously varied and an IVT is a transmission whose ratio can be continuously varied from positive to negative values. However, efficient use of such transmissions in walking applications requires changing the transmission ratio at a minimal energy cost, even at rest, i.e. when the input shaft is not rotating. This contribution proposes a novel CVT and IVT principle which can achieve such ratio variations at rest. The presented CVT is a modified planetary gear, whose planets are conical and mounted on inclined shafts, and whose ring is made of contiguous diabolo-shaped rollers. This configuration enables the control of the transmission ratio by adjusting the point of contact between the cones and rollers that comprise the ring. A traditional planetary gear system can be added to the CVT to form an IVT.

Bipedal walking with humanoid robots requires efficient real-time control. Nowadays, most bipedal robots require to ensure local stability at every instant in time, preventing them from achieving the impressive human walking skills. At the same time, bio-inspired walking controllers are emerging, though they are still mostly explored in simulation studies. However, porting these controllers to real hardware is needed to validate their use on real robots, as well as adapting them to face the world non-idealities. Here, we implemented one of them on a real humanoid robot, namely the COMAN, by conducting dynamic walking experiments. More precisely, we used a muscle-reflex model producing efficient and humanlike gaits. Starting from an off-line optimization performed in simulation, we present the controller implementation, focussing on the additional steps required to port it to real hardware. In our experimental results, we highlight some discrepancies between simulation and reality, together with possible controller extensions to fix them. Despite these differences, the real robot still managed to perform dynamic walking. On top of that, its gait exhibited stretched legs and foot roll at some points of the gait, two human walking features hard to achieve with most robot gaits. We present this on a 50 steps walk where the robot was free to move in the sagittal plane while lateral balance was provided by a human operator.

Estimation of the temporal derivatives of a noisy position signal is a ubiquitous problem in industrial and robotics engineering. Here, we propose a new approach to get velocity and acceleration estimates of cyclical/periodic signals near to steady-state regime, by using adaptive oscillators. Our method combines the advantages of introducing no delay, and filtering out the high-frequency noise. We expect this method to be useful in control applications requiring undelayed but smooth estimates of velocity and acceleration (e.g., velocity control and inverse dynamics) of quasi-periodic tasks (e.g., active vibration compensation, robot locomotion, and lower-limb movement assistance).

Series elastic actuators are very popular in rehabilitation robotics. Among other advantages, elastic elements between the actuator and the load permit to store and release energy during the task completion, such that the energy balance is improved and the motor power peak is decreased. In rhythmic tasks like walking, this reduces to design the spring stiffness such that it works at resonance. To comply with different gaits and cadences, it is therefore necessary to design Variable Stiffness Actuators (VSA). This paper proposes three contributions: (i) we apply a particular concept of VSA to an active ankle prosthesis; (ii) we discuss the relevance of using VSA to change the stiffness also within the gait cycle; and (iii) we elaborate some control strategies for this device. Our guideline is to track a mechanical design and a controller maximizing energy efficiency. We establish that a promising approach is simply to control the amount of energy stored in the elastic element.

Inferring temporal derivatives (like velocity and acceleration) from a noisy position signal is a well-known challenge in control engineering, due to the intrinsic trade-off between noise filtering and estimation bandwidth. To tackle this problem, in this paper we propose a new approach specifically designed for periodic movements. This approach uses an adaptive oscillator as fundamental building block. It is a tool capable of synchronizing to a periodic input while learning its features (frequency, amplitude, ...) in dedicated state variables. Since the oscillator's input and output are perfectly synchronized during steady-state regime, a non-delayed estimate of the input temporal derivatives can be obtained simply by deriving the output analytical form. Pending a (quasi-)periodic input signal, these temporal derivatives are thus synchronized with the actual kinematics, while the signal bandwidth can be arbitrarily tuned by the intrinsic dynamics of the oscillator. We further validate this approach by developing an impedance-based strategy for assisting human walking in the LOPES lower-limb exoskeleton. Preliminary results with a single participant give rise to three main conclusions. First, our method indeed provides velocity and acceleration estimates of the participant's joint kinematics which are smoother and less delayed with respect to the actual kinematics than using a standard Kalman filter. Second, closing the human-robot loop with a high-gain impedance field depending on the acceleration is not possible with a Kalman filter approach, due to unstable dynamics. In contrast, our approach tolerates high gains (up to 70% of the nominal walking torque), showing its intrinsic stability. Finally, no clear benefit of the acceleration-dependent field with respect to a simpler position-dependent field is visible regarding the reduction of metabolic cost. This last result illustrates the challenge of designing sound assistive strategies for complex tasks like walking.

Current research on discrete and rhythmic movements differs in both experimental procedures and theory, despite the ubiquitous overlap between discrete and rhythmic components in everyday behaviors. Models of rhythmic movements usually use oscillatory systems mimicking central pattern generators (CPGs). In contrast, models of discrete movements often employ optimization principles, thereby reflecting the higher-level cortical resources involved in the generation of such movements. This letter proposes a unified model for the generation of both rhythmic and discrete movements. We show that a physiologically motivated model of a CPG can not only generate simple rhythmic movements with only a small set of parameters, but can also produce discrete movements if the CPG is fed with an exponentially decaying phasic input. We further show that a particular coupling between two of these units can reproduce main findings on in-phase and antiphase stability. Finally, we propose an integrated model of combined rhythmic and discrete movements for the two hands. These movement classes are sequentially addressed in this letter with increasing model complexity. The model variations are discussed in relation to the degree of recruitment of the higher-level cortical resources, necessary for such movements.

The paper considers the feedback stabilization of periodic orbits in a planar juggler. The juggler is “blind,” i.e, he has no other sensing capabilities than the detection of impact times. The robustness analysis of the proposed control suggests that the arms acceleration at impact is a crucial design parameter even though it plays no role in the stability analysis. Analytical results and convergence proofs are provided for a simplified model of the juggler. The control law is then adapted to a more accurate model and validated in an experimental setup.

The paper studies the properties of a sinusoidally vibrating wedge billiard as a model for 2-D bounce juggling. It is shown that some periodic orbits that are unstable in the elastic fixed wedge become exponentially stable in the nonelastic vibrating wedge. These orbits are linked with certain classical juggling patterns, providing an interesting benchmark for the study of the frequency-locking properties in human rhythmic tasks. Experimental results on sensorless stabilization of juggling patterns are described.