In this paper, we introduced a conceptual analysis to select stability features when performing predefined and precise motions on robots. By analyzing the different stable poses named features and the possible transitions towards different ones, the introduced concept allows to design more predictable and suitable motions when performing particular tasks. As an example of how the concept can be applied we use it on the fall recovery of the quadruped robot CENTAURO. This robot, which is equipped with a custom hybrid wheel-legged mobility system, have good intrinsic stability as other quadrupeds. However, the characteristics of the rough terrains where it might be deployed require complex maneuvers to cope with possible strong disturbances. To prevent and more importantly recover from falls, realignment of postural responses will not be adequate, and effective recovery procedures should be developed. This paper introduces the details of how the presented conceptual analysis provides and an effective fall recovery routine for CENTAURO based on a state machine. The performance of the proposed approach is evaluated with extensive simulation trials using the dynamic model of the CENTAURO robot showing good effectiveness in recovering the robot after fall on flat and inclined surfaces.

This paper presents a comparison study of three control design approaches for humanoid balancing based on the Center of Mass ($CoM$) stabilization and body posture adjustment. The comparison was carried out under controlled circumstances allowing other researchers to replicate and compare our results with their own. The feedback control from state space design is based on simple models and provides sufficient robustness to control complex and high Degrees of Freedom (DoFs) systems, such as humanoids. The implemented strategies allow compliant behavior of the robot in reaction to impulsive or periodical disturbances, resulting in a smooth and human-like response while considering constraints. In this respect, we implemented two balancing strategies to compensate for the $CoM$ deviation. The first one uses the robot’s capture point as a stability principle and the second one uses the Force/Torque sensors at the ankles to define a $CoM$ reference that stabilizes the robot. In addition, was implemented a third strategy based on upper body orientation to absorb external disturbances and counterbalance them. Even though the balancing strategies are implemented independently, they can be merged to further increase balancing performance. The proposed strategies were previously applied on different humanoid bipedal platforms, however, their performance could not be properly benchmarked before. With this concern, this paper focuses on benchmarking in controlled scenarios to help the community in comparing different balance techniques. The key performance indicators (KPIs) used in our comparison are the $CoM$ deviation, the settling time, the maximum measured orientation, passive gait measure, measured ankles torques, and reconstructed Center of Pressure ($CoP$). The benchmarking experiments were carried out in simulations and using the facility at Istituto Italiano di Tecnologia on the REEM-C humanoid robot provided by PAL robotics inside the EU H2020 project EUROBENCH framework.

Soft robotics is becoming an emerging solution to many of the problems in robotics, such as weight, cost and human interaction. In order to overcome such problems, bio-inspired designs have introduced new actuators, links and architectures. However, the complexity of the required models for control has increased dramatically and geometrical model approaches, widely used to model rigid dynamics, are not enough to model these new hardware types. In this paper, different linear and non-linear models will be used to model a soft neck consisting of a central soft link actuated by three motor-driven tendons. By combining the force on the different tendons, the neck is able to perform a motion similar to that of a human neck. In order to simplify the modeling, first a system input–output redefinition is proposed, considering the neck pitch and roll angles as outputs and the tendon lengths as inputs. Later, two identification strategies are selected and adapted to our case: set membership, a data-driven, nonlinear and non-parametric identification strategy which needs no input redefinition; and Recursive least-squares (RLS), a widely recognized identification technique. The first method offers the possibility of modeling complex dynamics without specific knowledge of its mathematical representation. The selection of this method was done considering its possible extension to more complex dynamics and the fact that its impact in soft robotics is yet to be studied according to the current literature. On the other hand, RLS shows the implication of using a parametric and linear identification in a nonlinear plant, and also helps to evaluate the degree of nonlinearity of the system by comparing the different performances. In addition to these methods, a neural network identification is used for comparison purposes. The obtained results validate the modeling approaches proposed.

Relearning to walk requires progressive training in real scenarios—overground—along with assistance in basic tasks, such as balancing. In addition, user ability must be maximized through compliant robotic assistance as needed. Despite decades of research, gait rehabilitation robotic devices yield controversial results. This article presents the conceptual design of a novel walking assistance and rehabilitation robot, the NIMBLE robot, aimed at providing ambulatory, bodyweight-supported gait training, assisting the user’s center of mass trajectory to aid weight transfer and dynamic balance during walking. NIMBLE consists of a robotic mobile frame, a partial bodyweight support (PBWS) system, an ambulatory lower-limb exoskeleton (Exo-H3) and a cable-driven pelvis-assisting robot. Designed as a modular structure, it differentiates hierarchical communication levels through a Robot Operating System (ROS) 2 network. We present the mechatronic design and experimental results assessing the impact of the mechatronic coupling between the robotic modules on the walking kinematics and the frame movement control performance. The robotic frame hardly affects the walking kinematics up to 2 degrees in both the sagittal and frontal planes, making it feasible for lateral balance and weight translation training. Moreover, it successfully tracks and follows user trajectories. The NIMBLE robotic frame assessment shows promising results for ambulatory gait rehabilitation.

Adaptation is a desirable feature when dealing with the identification of complex systems. However, this property can be difficult to achieve when non-convex model structures such as neural networks are employed to parametrize the unknown system. This work introduces a novel approach for dynamically adapting the data set that defines the model in non-parametric Set Membership identification methods. The proposed solution constructs a nonparametric, nonlinear model of a discrete-time dynamical system by exploring the data set, assuming the system follows a Nonlinear Auto-Regressive model with exogenous Inputs (NARX) structure. The identification data are assumed to be affected by unknown but bounded noise. Specifically, two strategies are proposed to adapt the identification data set while preserving system performance dynamically. The first strategy allows the data set to incorporate new data as novel modeling information becomes available, while redundant information can be eliminated when memory conditions are reached. The second strategy introduces new information sequentially; once an auxiliary memory vector in the data set reaches its desired cardinality, the method orderly replaces the oldest data with newer dynamics. These strategies enable the identified models to adapt in response to unmodeled behaviors arising from time-varying dynamics or limited initial data sets, minimizing the need for extensive experimentations and allowing to dynamically reconstruct the data set for developing data-driven models. The effectiveness of the proposed approaches is demonstrated through the experimental modeling of a nonlinear mechatronic system. Performance is benchmarked against neural network models and a static Set Membership identification strategy. Results indicate that the proposed dynamic data set generation approach improves the accuracy and robustness of the model when using non-informative experimental data sets as starting point for the estimation, improving the overall performance of the data-driven modeling task and facilitating the use of these modeling techniques in real environments.

Developing robust benchmarking methods is crucial for evaluating the standing stability of bipedal systems, including humanoid robots and exoskeletons. This paper presents a standardized benchmarking procedure based on the Linear Inverted Pendulum Model and the Capture Point concept to normalize the maximum angular momentum before falling. Normalizing these variables establishes absolute and relative benchmarks achieving comprehensive comparisons across different bipedal systems. Simulations were conducted on two humanoid robots, COMAN and WALK-MAN, to validate the approach, demonstrating its applicability to robots of various sizes and configurations. Furthermore, the same benchmarking method was applied to the therapeutic exoskeleton H3, illustrating its potential to optimize mechanical design and therapeutic performance. The results indicate that this standardized procedure provides a valuable tool for assessing and improving the stability of anthropomorphic robotic systems, providing insights into both hardware capabilities and control strategies.