Kinematic inversion via closed-loop schemes is at the core of robotic manipulators' success. These algorithms robustly and efficiently compute a reference for low-level controllers to position the end effector or other selected parts of the robot's body in space. This work concerns the extension of kinematic inversion algorithms beyond fully actuated systems into the realm of underactuated robotics. We revise a well-known kinematic inversion algorithm in continuum (soft) robotics, the direct task-to-actuators inversion, and generalize it beyond this field and its current strong assumptions. More precisely, in its existing formulation, the algorithm relies on geometrical assumptions that hold valid only if the robot is fully actuated. We leverage recent advancements in system dynamics of mechanical systems to generalize the method beyond soft robotics and provide a constructive strategy that does not rely on any full actuation assumption. After that, we propose a second closed-loop inversion algorithm that works in even more general conditions, not requiring the explicit derivation of end-to-end actuators to task space mapping.@en

While consciousness has been historically a heavily debated topic, awareness had less success in raising the interest of scholars. However, more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially generated. The landscape is rapidly evolving, with multiple voices and interpretations of the concept being conceived and techniques being developed. The goal of this paper is to summarize and discuss the ones among these voices connected with projects funded by the EIC Pathfinder Challenge “Awareness Inside” callwithin Horizon Europe, designed specifically for fostering research on natural and synthetic awareness. In this perspective, we dedicate special attention to challenges and promises of applying synthetic awareness in robotics, as the development of mature techniques in this new field is expected to have a special impact on generating more capable and trustworthy embodied systems.

@en

Stepping strategy, including step time and step location modulation, and hip strategy, i.e., upper-body movement, play crucial roles in achieving robust humanoid locomotion. However, exploiting these balance strategies in a unified and flexible manner has not been well addressed. In this article, we propose a sequential convex optimization approach. Based on the linear inverted pendulum model, we modulate step parameters, including step location and step time, using quadratically constrained quadratic programming in real time. Then, based on the nonlinear inverted pendulum plus flywheel model, we regulate angular momentum using the linear model predictive control. To accommodate for scenarios with height variation, we consider nonlinear 3-D locomotion dynamics explicitly. The proposed approach is validated via comparison studies and extensive experiments on the humanoid with planar and linear feet. The results demonstrate enhanced robustness against dynamic disturbances and adaptability to real-world scenarios. On average, the enhanced stepping strategy rejects 135% larger external forces than our previous article. Also, robust locomotion across height-varying stepping stones is realized, which is rarely reported for a humanoid robot with planar feet.

@en

Deep reinforcement learning (DRL) has emerged as a promising solution to mastering explosive and versatile quadrupedal jumping skills. However, current DRL-based frameworks usually rely on pre-existing reference trajectories obtained by capturing animal motions or transferring experience from existing controllers. This work aims to prove that learning dynamic jumping is possible without relying on imitating a reference trajectory by leveraging a curriculum design. Starting from a vertical in-place jump, we generalize the learned policy to forward and diagonal jumps and, finally, we learn to jump across obstacles. Conditioned on the desired landing location, orientation, and obstacle dimensions, the proposed approach yields a wide range of omnidirectional jumping motions in real-world experiments. In particular, we achieve a 90 cm forward jump, exceeding all previous records for similar robots. Additionally, the robot can reliably execute continuous jumping on soft grassy grounds, which is especially remarkable as such conditions were not included in the training stage.

@en

Aerial robots are a well-established solution for environmental surveying, exploration, and inspection, thanks to their superior maneuverability and agility. Nowadays, the algorithms that provide these capabilities rely on GNSS and Vision, which are obstructed in some environments of interest, e.g., indoors and underground or in smoke and dust. In similar conditions, animals rely on the sense of touch and compliant responses to interactions embodied in the body morphology. This way, they can navigate safely using tactile cues by feeling the environment surrounding them. In this work, we take inspiration from the natural example and propose an approach that allows a quadrotor to navigate using tactile information from the environment. We propose to endow a conventional quadrotor with a novel robotic finger that embodies compliance and sensing capabilities. We complete this design with a navigation approach that generates new waypoints based on the robotic finger's contact information to follow the unknown environment. The overall system's evaluation shows successful, repeatable results in 36 flight experiments with various relative angles between the drone and a piecewise planar surface.

@en

Suitable representations of dynamical systems can simplify their analysis and control. On this line of thought, this article aims to answer the following question: Can a transformation of the generalized coordinates under which the actuators directly perform work on a subset of the configuration variables be found? We not only show that the answer to this question is yes but also provide necessary and sufficient conditions. More specifically, we look for a representation of the configuration space such that the right-hand side of the dynamics in Euler-Lagrange form becomes [\boldsymbol{I}\; \boldsymbol{O}]{T}\boldsymbol{u}, being \boldsymbol{u} the system input. We identify a class of systems, called collocated, for which this problem is solvable. Under mild conditions on the input matrix, a simple test is presented to verify whether a system is collocated or not. By exploiting power invariance, we provide necessary and sufficient conditions that a change of coordinates decouples the input channels if and only if the dynamics is collocated. In addition, we use the collocated form to derive novel controllers for damped underactuated mechanical systems. To demonstrate the theoretical findings, we consider several Lagrangian systems with a focus on continuum soft robots.

@en

Formulating the dynamics of continuously deformable objects and other mechanical systems analytically from first principles is an exceedingly challenging task, often impractical in real-world scenarios. What makes this challenge even harder to solve is that, usually, the object has not been observed previously, and the only information that we can get from it is a stream of RGB camera data. In this study, we explore the use of deep learning techniques to solve this nonlinear identification problem. We specifically focus on extracting dynamic models of simple deformable objects from the high-dimensional sensor input coming from an RGB camera. We investigate a two-stage approach to achieve this goal. First, we train a variational autoencoder to extract an extremely low-dimensional representation of the object configuration. Then, we learn a dynamic model that predicts the evolution of these latent space variables. The proposed architecture can accurately predict the object's state up to one second into the future.

@en

Creating artificial general intelligence is the solution most often in the spotlight. It is also linked with the possibility—or fear—of machines gaining consciousness. Alternatively, developing domain-specific artificial intelligence is more reliable, energy-efficient, and ethically tractable, and raises mostly a problem of effective coordination between different systems and humans. Herein, it is argued that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient.

@en

Adopting compliant structures holds the potential to enhance the robustness and interaction capabilities of the next generation of bionic limbs. Although researchers have proficiently explored this approach in the design of artificial hands, they devoted little attention to the development of more proximal joints. This work presents a compliant prosthetic elbow prototype called BICEP. The design incorporates compliant cross-axis flexural pivots that connect the upper and lower arm without direct contact between the two links. The actuation architecture, inspired by biological mechanisms, employs one actuator and two tendons to create an agonist-antagonist mechanism. This joint enables rotation along its flexion-extension axis while maintaining flexibility in out-of-plane directions, in a system with an overall weight of 351 g. A preliminary evaluation showcases lifting capacities supporting up to 2500 g, and a maximum speed of 157^∘ per second across a 135^∘ range of motion. The soft cross-axis flexural pivots exhibit compliant behavior in both the sagittal and transversal planes, enabling a pleasant interaction with the environment and ensuring safe absorption of unintentional impacts.

@en

Soft continuum manipulators are celebrated for their versatility and physical robustness to external forces and perturbations. However, this feature comes at a cost. The many degrees of freedom and compliance pose challenges for accurate pose reconstruction, both in terms of distributed sensing and pose reconstruction algorithms. Moreover, soft arms are inherently susceptible to deformation from external forces or loads, meaning that closed-loop control is essential for robust task performance. In this article, we propose the integration of multiple Inertial Measurement Units (IMUs) of a soft robot arm, Helix, for reconstruction of pose under internal and external forces. Furthermore, we integrate this dynamic pose reconstruction for kinematic-based closed-loop control strategies. By serially integrating sensing in the body of the Helix soft manipulator, we provide the system with high-frequency pose reconstruction and demonstrate improvements in end effector position with comparison to open-loop performance.

@en

Robot feet are crucial for maintaining dynamic stability and propelling the body during walking, especially on uneven terrains. Traditionally, robot feet were mostly designed as flat and stiff pieces of metal, which meets its limitations when the robot is required to step on irregular grounds, e.g., stones. While one could think that adding compliance under such feet would solve the problem, this is not the case. To address this problem, we introduced the SoftFoot, an adaptive foot design that can enhance walking performance over irregular grounds. The proposed design is completely passive and varies its shape and stiffness based on the exerted forces, through a system of pulley, tendons, and springs opportunely placed in the structure. This article outlines the motivation behind the SoftFoot and describes the theoretical model which led to its final design. The proposed system has been experimentally tested and compared with two analogous conventional feet, a rigid one and a compliant one, with similar footprints and soles. The experimental validation focuses on the analysis of the standing performance, measured in terms of the equivalent support surface extension and the compensatory ankle angle, and the rejection of impulsive forces, which is important in events such as stepping on unforeseen obstacles. Results show that the SoftFoot has the largest equivalent support surface when standing on obstacles, and absorbs impulsive loads in a way almost as good as a compliant foot.

@en

Robotics is entering our daily lives. The discipline is increasingly crucial in fields such as agriculture, medicine, and rescue operations, impacting our food, health, and planet. At the same time, it is becoming evident that robotic research must embrace and reflect the diversity of human society to address these broad challenges effectively. In recent years, gender inclusivity has received increasing attention, but it still remains a distant goal. In addition, awareness is rising around other dimensions of diversity, including nationality, religion, and politics. Unfortunately, despite the efforts, empirical evidence shows that the field has still a long way to go before achieving a sufficient level of equality, diversity, and inclusion across these spectra. This study focuses on the soft robotics community?a growing and relatively recent subfield?and it outlines the present state of equality and diversity panorama in this discipline. The article argues that its high interdisciplinary and accessibility make it a particularly welcoming branch of robotics. We discuss the elements that make this subdiscipline an example for the broader robotic field. At the same time, we recognize that the field should still improve in several ways and become more inclusive and diverse. We propose concrete actions that we believe will contribute to achieving this goal, and provide metrics to monitor its evolution.@en

While much work has been done recently in the realm of model-based control of soft robots and soft-rigid hybrids, most works examine robots that have an inherently serial structure. While these systems have been prevalent in the literature, there is an increasing trend toward designing soft-rigid hybrids with intrinsically coupled elasticity between various degrees of freedom. In this work, we seek to address the issues of modeling and controlling such structures, particularly when underactuated. We introduce several simple models for elastic coupling, typical of those seen in these systems. We then propose a controller that compensates for the elasticity, and we prove its stability with Lyapunov methods without relying on the elastic dominance assumption. This controller is applicable to the general class of underactuated soft robots. After evaluating the controller in simulated cases, we then develop a simple hardware platform to evaluate both the models and the controller. Finally, using the hardware, we demonstrate a novel use case for underactuated, elastically coupled systems in "sensorless"force control.

@en

To promote the research in compliant quadrupedal locomotion, especially with parallel elasticity, we present Delft E-Go, which is an easily accessible quadruped that combines the Unitree Go1 with open-source mechanical add-ons and control architecture. Implementing this novel system required a combination of technical work and scientific innovation. First, a dedicated parallel spring with adjustable rest length is designed to strengthen each actuated joint. Then, a novel 3-D dual spring-loaded inverted pendulum model is proposed to characterize the compliant locomotion dynamics, decoupling the actuation with parallel compliance. Based on this template model, trajectory optimization is employed to generate optimal explosive motion without requiring reference defined in advance. To complete the system, a torque controller with anticipatory compensation is adopted for motion tracking. Extensive hardware experiments in multiple scenarios, such as trotting across uneven terrains, efficient walking, and explosive pronking, demonstrate the system’s reliability, energy benefits of parallel compliance, and enhanced locomotion capability. Particularly, we demonstrate for the first time the controlled pronking of a quadruped with asymmetric legs.

@en

Integrating Brain-Machine Interfaces into non-clinical applications like robot motion control remains difficult - despite remarkable advancements in clinical settings. Specifically, EEG-based motor imagery systems are still error-prone, posing safety risks when rigid robots operate near humans. This work presents an alternative pathway towards safe and effective operation by combining wearable EEG with physically embodied safety in soft robots. We introduce and test a pipeline that allows a user to move a soft robot's end effector in real time via brain waves that are measured by as few as three EEG channels. A robust motor imagery algorithm interprets the user's intentions to move the position of a virtual attractor to which the end effector is attracted, thanks to a new Cartesian impedance controller. We specifically focus here on planar soft robot-based architected metamaterials, which require the development of a novel control architecture to deal with the peculiar nonlinearities - e.g., non-affinity in control. We preliminarily but quantitatively evaluate the approach on the task of setpoint regulation. We observe that the user reaches the proximity of the setpoint in 66% of steps and that for successful steps, the average response time is 21.5s. We also demonstrate the execution of simple real-world tasks involving interaction with the environment, which would be extremely hard to perform if it were not for the robot's softness.@en

People suffering from conditions affecting their activities of daily living and those who do straining repetitive tasks could be assisted using supportive devices. These devices have generally been stiff in design, with more recent advances exploring soft suits, removing the need for heavier structural components. These supportive devices are often fitted with rigid actuators that lack inherent compliance and rely on feedback to regulate the assistive force. Compl iant actuators able to control stiffness and pretension have only been applied in rigid assistive devices with these devices being designed for controllable stiffness in rotation and not linear motion. This work briefly presents the results of a user study on the effects of a compliant actuator in a soft supportive device for arm flexion, the development and testing of a variable linear stiffness mechanism for a linear motion capable of controlling the stiffness and equilibrium position, and the integration of said actuator in an exosuit.

@en

As global demand for fruits and vegetables continues to rise, the agricultural industry faces significant challenges in securing adequate labor. Robotic harvesting devices offer a promising solution to address this issue. Harvesting delicate fruits, such as blackberries, presents unique open challenges due to their fragility. This paper introduces BerryTwist, a prototype robotic gripper specifically designed for blackberry harvesting. The gripper features a fabric tube mechanism that uses motorized twisting action to gently envelop the fruit, ensuring uniform pressure application and minimizing damage. The twisting motion is transferred to the tube through a compliant mechanism, thus maintaining the overall softness of the structure. We thoroughly tested BerryTwist, paying particular attention to the effect of varying tube properties. We developed three types of tubes varying in elasticity and compressibility, using foam padding, spandex, and food-safe cotton cheesecloth. Performance testing focused on assessing each gripper's ability to detach and release blackberries, with an emphasis on quantifying damage rates. The results indicate that the proposed gripper achieved an 82% success rate in detaching blackberries and a 95% success rate in releasing them, demonstrating its promising potential for robotic harvesting applications. Finally, we will demonstrate the robotic harvesting operation by establishing a simple farm setup and integrating the gripper with Franka Emika's robot manipulator.

@en

Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which employ as few DoFs as possible while still accurately capturing the core characteristics of the theoretically infinite-dimensional dynamics. However, there is no quantitative way to measure if the ROM of a soft robot has succeeded in this task. In other fields, like structural dynamics or flexible link robotics, linear normal modes are routinely used to this end. Yet, this theory is not applicable to soft robots due to their nonlinearities. In this work, we propose to use the recent nonlinear extension in modal theory -called eigenmanifolds- as a means to evaluate control-oriented models for soft robots and compare them. To achieve this, we propose three similarity metrics relying on the projection of the nonlinear modes of the system into a task space of interest. We use this approach to compare quantitatively, for the first time, ROMs of increasing order generated under the piecewise constant curvature (PCC) hypothesis with a high-dimensional finite element (FE)-like model of a soft arm. Results show that by increasing the order of the discretization, the eigenmanifolds of the PCC model converge to those of the FE model.

@en

We identify the nonlinear normal modes spawning from the stable equilibrium of a double pendulum under gravity, and we establish their connection to homoclinic orbits through the unstable upright position as energy increases. This result is exploited to devise an efficient swing-up strategy for a double pendulum with weak, saturating actuators. Our approach involves stabilizing the system onto periodic orbits associated with the nonlinear modes while gradually injecting energy. Since these modes are autonomous system evolutions, the required control effort for stabilization is minimal. Even with actuator limitations of less than 1% of the maximum gravitational torque, the proposed method accomplishes the swing-up of the double pendulum by allowing sufficient time.

@en

Introducing parallel elasticity in the hardware design endows quadrupedal robots with the ability to perform explosive and efficient motions. However, for this kind of articulated soft quadruped, realizing dynamic jumping with robustness against system uncertainties remains a challenging problem. To achieve this, we propose an impact-aware jumping planning and control approach. Specifically, an offline kino-dynamic-type trajectory optimizer is first formulated to achieve compliant 3D jumping motions, using a novel actuated spring-loaded inverted pendulum (SLIP) model. Then, an optimization-based online landing strategy, including pre-impact leg motion modulation and post-impact landing recovery, is designed. The actuated SLIP model, with the capability of explicitly characterizing parallel elasticity, captures the jumping and landing dynamics, making the problem of motion generation/regulation more tractable. Finally, a hybrid torque control consisting of a feedback tracking loop and a feedforward compensation loop is employed for motion control. Experiments demonstrate the ability to accomplish robust 3D jumping motions with stable landing and recovery. Besides, our approach can be applied to quadrupedal robots with or without additional parallel compliance.

@en