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.

This work introduces a new sensing system for biped robots based on plantar robot skin, which provides not only the resultant forces applied on the ankles but a precise shape of the pressure distribution in the sole together with other extra sensing modalities (temperature, pre-touch and acceleration). The information provided by the plantar robot skin can be used to compute the center of pressure and the ground reaction forces. This information also enables the online construction of the supporting polygon and its preemptive shape before foot landing using the proximity sensors in the robot skin. Two experiments were designed to show the advantages of this new sensing technology for improving balance and walking controllers for biped robots over unknown terrain.

In this letter, we present a fast method for autonomously planing manipulation tasks for mobile manipulators. The planner defines an optimal order to perform pick-and-place operations for taking objects from a cluttered scene to specific deposit areas considering both, manipulator and mobile base motion. Our method first examines the grasping feasibility of the objects with an inverse reachability map. Then, it defines all the placing locations for the objects and analyses the corresponding preconditions to reach them. Finally, it defines a sequence for rearranging the objects that minimize the execution time. We take advantage of the environment's underlying combinatorial structure to define the shortest path. In this work, we consider both the monotone case, where each object may be moved at most once, and the non-monotone cases. An experimental evaluation on the Human Support Robot (HSR) shows the effectiveness of our solutions and the scalability of our method as the number of objects increases for both cases. Tests on monotone problem instances with 20 objects show the proposed method can save up to 17% base traveling time when comparing to baseline methods at the cost of less than 9 seconds planning time. In a test on a simple non-monotone instance, the proposed method further reduces the total execution time by 25% by minimizing the total number of actions in a few seconds of planning time. The characteristic speed in re-planning makes our method suitable for online usage.

Skin can provide rich multi-modal contact information about the interaction forces of a robot with its environment. With this new way of sensing, a new generation of compliant controllers can be developed to enable different kinds of interactions. In this paper, a pressure-driven compliance controller is presented to generate virtual compliant forces depending on both external contact forces and their area of contact. The proposed controller can be adapted for any skin technology that provides a spatial distribution of force sensors over the surface of a robot and the geometry of the contact areas. This modality of body compliance is formulated as a task function which can be inserted in strict or non-strict hierarchical task execution policies. The controller was tested on the upper body of a Humanoid robot (16 DoF) covered with robot skin.

In this work, we present a novel method for ankle/foot compliance for biped humanoid robots walking over uneven terrain. Based on distributed plantar proximity sensing, we developed the Preemptive Foot Compliance (PFC) control that generates a Preemptive Ground Reaction Wrench that modifies the foot orientation to maximize the largest contact area to reduce the impact force produced at landing. PFC can be easily included in any walking controller for flat ground and become capable of walking on uneven terrains. The PFC was tested on two full-size humanoid robots running two different walking controllers, originally designed only for flat-ground walking. Both robots increase their capability to walk over uneven terrain and the walking impacts in flat grown were reduced by approximately 80%.