This article presents a holistic approach to the engineering of an artificial robot skin for robots. An example of a multimodal skin cell is given, one that supports multiple human-like sensing modalities, and support for skin cell network is also provided; this is essential to form large-area skin patches in order to cover the surfaces of robots. The essential elements of efficiently handling a large amount of tactile data are explained. A general control framework, which supports robots commanded in position, velocity, and torque, is provided and validated. Several applications of this robot skin will be presented, demonstrating the effectiveness and efficiency of our artificial robot skin to support a wide number of robotic platforms as well as its ease of use across different domains.

This work aims at reviewing, analyzing and comparing a range of state-of-the-art approaches to inertial parameter identification in the context of robotics. We introduce “BIRDy (Benchmark for Identification of Robot Dynamics)”, an open-source Matlab toolbox, allowing a systematic and formal performance assessment of the considered identification algorithms on either simulated or real serial robot manipulators. Seventeen of the most widely used approaches found in the scientific literature are implemented and compared to each other, namely: the Inverse Dynamic Identification Model with Ordinary, Weighted, Iteratively Reweighted and Total Least-Squares (IDIM-OLS, -WLS, -IRLS, -TLS); the Instrumental Variables method (IDIM-IV), the Maximum Likelihood (ML) method; the Direct and Inverse Dynamic Identification Model approach (DIDIM); the Closed-Loop Output Error (CLOE) method; the Closed-Loop Input Error (CLIE) method; the Direct Dynamic Identification Model with Nonlinear Kalman Filtering (DDIM-NKF), the Adaline Neural Network (AdaNN), the Hopfield-Tank Recurrent Neural Network (HTRNN) and eventually a set of Physically Consistent (PC-) methods allowing the enforcement of parameter physicality using Semi-Definite Programming, namely the PC-IDIM-OLS, -WLS, -IRLS, PC-IDIM-IV, and PC-DIDIM. BIRDy is robot-agnostic and features a complete inertial parameter identification pipeline, from the generation of symbolic kinematic and dynamic models to the identification process itself. This includes functionalities for excitation trajectory computation as well as the collection and pre-processing of experiment data. In this work, the proposed methods are first evaluated in simulation, following a Monte Carlo scheme on models of the 6-DoF TX40 and RV2SQ industrial manipulators, before being tested on the real robot platforms. The robustness, precision, computational efficiency and context of application the different methods are investigated and discussed.

In this paper, we propose a control method, providing mobile robots with whole-body compliance capabilities, in response to multicontact physical interactions with their environment. The external forces applied to the robot, as well as their localization on its kinematic tree, are measured using a multimodal, self-configuring, and self-calibrating artificial skin. We formulate a compliance control law in Cartesian space, as a set of quadratic optimization problems, solved in parallel for each limb involved in the interaction process. This specific formulation makes it possible to determine the torque commands required to generate the desired reactive behaviors, while taking the robot kinematic and dynamic constraints into account. When a given limb fails to produce the desired compliant behavior, the generalized force residual at the considered contact points is propagated to a parent limb in order to be adequately compensated. Hence, the robot's compliance range can be extended in a both robust and easily adjustable manner. The experiments performed on a dual-arm velocity-controlled mobile manipulator show that our methodology is robust to nullspace interactions and robot physical constraints.

Postural instability is a disabling yet insufficiently characterized feature of Schizophrenia Spectrum Disorders (SSD). Quantitative posturography has consistently demonstrated increased body sway in SSD, which correlates with negative symptom severity and functional impairment; however, the sensorimotor mechanisms underlying these abnormalities remain poorly understood. This gap arises in part because conventional sway metrics neglect the multiscale, orientation-dependent organization of postural control. To overcome these limitations, we introduce Frequency-Specific Oriented Fractal Scaling Component Analysis (FS-OFSCA), a novel method which partitions center-of-pressure (CoP) trajectories into physiologically motivated frequency bands associated with proprioceptive and visual-vestibular processing. Within each band, FS-OFSCA identifies the planar directions of strongest and weakest long-range temporal correlations. We applied this approach to CoP recordings obtained from 42 individuals with SSD and 33 Healthy Participants (HP), across 12 stance conditions. The central result is that SSD is characterized less by how much participants sway than by a stance- and band-specific reorganization of the directional geometry of sway. Using linear mixed-effects models of the inter-axial angle ( $$\Delta \theta$$ ), SSD showed a compression of $$\Delta \theta$$ that was statistically supported at the overall-group level in the global band ( $$p = 0.001$$ ; Holm-adjusted $$p = 0.003$$ ); in the visual-vestibular and proprioceptive bands the overall group effect was not significant and the difference was stance-dependent. The coupling between $$\Delta \theta$$ and directional fractal descriptors ( $$H_1$$ , $$H_2$$ , $$SD_H$$ ) further differed between groups in a band-dependent manner. By comparison, conventional geometric, spectral, and entropy-based metrics also separated the groups, with small-to-moderate effect sizes (Hedges’ $$g \approx 0.1-0.5$$ ) concentrated under sensory-challenging conditions; FS-OFSCA captures a complementary, directional aspect of postural control rather than information categorically unavailable to those metrics. To support reproducible posturography, we release STABLE, an open-source Python toolkit integrating linear, spectral, nonlinear, and anisotropic analysis methods.