Showing papers by "Paolo Robuffo Giordano published in 2023"

On the Stability of Gated Graph Neural Networks

Abstract: In this paper, we aim to find the conditions for input-state stability (ISS) and incremental input-state stability ($\delta$ISS) of Gated Graph Neural Networks (GGNNs). We show that this recurrent version of Graph Neural Networks (GNNs) can be expressed as a dynamical distributed system and, as a consequence, can be analysed using model-based techniques to assess its stability and robustness properties. Then, the stability criteria found can be exploited as constraints during the training process to enforce the internal stability of the neural network. Two distributed control examples, flocking and multi-robot motion control, show that using these conditions increases the performance and robustness of the gated GNNs.

Trajectory-Based Shared Control with Integral Haptic Feedback

Abstract: HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Trajectory-Based Shared Control with Integral Haptic Feedback Firas Abi-Farraj, Riccardo Spica, Paolo Robuffo Giordano

Persistent Monitoring of Multiple Moving Targets Using High Order Control Barrier Functions

Abstract: This letter considers the problem of persistently monitoring a set of moving targets using a team of aerial vehicles. Each agent in the network is assumed equipped with a camera with limited range and Field of View (FoV) providing bearing measurements and it implements an Information Consensus Filter (ICF) to estimate the state of the target(s). The ICF can be proven to be uniformly globally exponentially stable under a Persistency of Excitation (PE) condition. We then propose a distributed control scheme that allows maintaining a prescribed minimum PE level so as to ensure filter convergence. At the same time, the agents in the group are also allowed to perform additional tasks of interest while maintaining a collective observability of the target(s). In order to enforce satisfaction of the observability constraint, we leverage two main tools: 1) the weighted Observability Gramian with a forgetting factor as a measure of the cumulative acquired information, and 2) the use of High Order Control Barrier Functions (HOCBF) as a mean to maintain a minimum level of observability for the targets. Simulation results are reported to prove the effectiveness of this approach.

Dynamical System-based Imitation Learning for Visual Servoing using the Large Projection Formulation

Abstract: Nowadays ubiquitous robots must be adaptive and easy to use. To this end, dynamical system-based imitation learning plays an important role. In fact, it allows to realize stable and complex robotic tasks without explicitly coding them, thus facilitating the robot use. However, the adaptation capabilities of dynamical systems have not been fully exploited due to the lack of closed-loop implementations making use of visual feedback. In this regard, the integration of visual information allows higher flexibility to cope with environmental changes. This work presents a dynamical system-based imitation learning for visual servoing, based on the large projection task priority formulation. The proposed scheme enables complex and stable visual tasks, as demonstrated by a simulation analysis and experiments with a robotic manipulator.

A Sensitivity-Aware Motion Planner (SAMP) to Generate Intrinsically-Robust Trajectories

Abstract: Closed-loop state sensitivity [1], [2] is a recently introduced notion that can be used to quantify deviations of the closed-loop trajectory of a robot/controller pair against variations of uncertain parameters in the robot model. While local optimization techniques are used in [1], [2] to generate reference trajectories minimizing a sensitivity-based cost, no global planning algorithm considering this metric to compute collision-free motions robust to parametric uncertainties has yet been proposed. The contribution of this paper is to propose a global control-aware motion planner for optimizing a state sensitivity metric and producing collision-free reference motions that are robust against parametric uncertainties for a large class of complex dynamical systems. Given the prohibitively high computational cost of directly minimizing the state sensitivity using asymptotically optimal sampling-based tree planners, the proposed RRT*-based SAMP planner uses an appropriate steering method to first compute a (near) time-optimal and kinodynamically feasible trajectory that is then locally deformed to improve robustness and decrease its sensitivity to uncertainties. The evaluation performed on planar/full-3D quadrotor UAV models shows that the SAMP method produces low sensitivity robust solutions with a much higher performance than a planner directly optimizing the sensitivity.

A Wearable Haptic Device for the Hand with Interchangeable End-Effectors.

Abstract: This paper presents a 4-degrees-of-freedom (4-DoF) hand wearable haptic device for Virtual Reality (VR). It is designed to support different end-effectors, that can be easily exchanged so as to provide a wide range of haptic sensations. The device is composed of a static upper body, secured to the back of the hand, and the (changeable) end-effector, placed in contact with the palm. The two parts of the device are connected by two articulated arms, actuated by four servo motors housed on the upper body and along the arms. The paper summarizes the design and kinematics of the wearable haptic device and presents a position control scheme able to actuate a broad range of end-effectors. As a proof of concept, we present and evaluate three representative end-effectors during interactions in VR, rendering the sensation of interacting (E1) with rigid slanted surfaces and sharp edges having different orientations, (E2) with curved surfaces having different curvatures, and (E3) with soft surfaces having different stiffness characteristics. A few additional end-effector designs are discussed. A human-subjects evaluation in immersive VR shows the broad applicability of the device, able to render rich interactions with a diverse set of virtual objects.