Pasquale Chiacchio

As human-robot interaction (HRI) rapidly spreads into numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs with humanoids, is generally not enough to ensure acceptance in physical interaction, as acceptance is directly linked to comfort and ergonomics, which are measured in terms of the quality of the robot movement perceived by the human. This article discusses the connection between comfort and the similarity of the robot movement to the human movement. By considering the kinematic characterization of human movement, this article focuses on the time laws of such movements, wherein the end-effector path is prescribed. Based on the lognormality principle for modeling human movements, a human-likeness index is defined and used to provide an a priori characterization of trajectories. Such an index can be used to evaluate the performance of trajectory generation algorithms in producing human-like movements before they are actually executed. For validation purposes, 68 subjects are required to judge their comfort. The results of three experimental campaigns involving a physical interaction with a robot demonstrate a globally consistent trend between the preference in terms of perceived comfort and the distribution of the suggested human-likeness index.

As human-robot interaction rapidly spreads in numerous fields, the subject of robot acceptance gains increasing importance. Visual similarity to the human body, as occurs for humanoids, is generally not enough to ensure acceptance in physical interaction, as acceptance directly links to comfort and ergonomics, which are measured in terms of the quality of the robot movement perceived by the human. This paper discusses the connection between comfort and similarity of the robot movement to the human one. By considering the kinematic characterization of human movement, this paper focuses on the time laws of such movements, wherein the end-effector path is prescribed. Based on the lognormality principle for modeling human movements, a human-likeness index is defined and used to provide an a priori characterization of trajectories. Such an index can be used to evaluate the performance of trajectory generation algorithms in producing human-like movements before they are actually executed. For validation purposes, 68 subjects are required to judge their comfort. The results of three experimental campaigns involving a physical interaction with a robot demonstrate a globally consistent trend between the preference in terms of perceived comfort and the distribution of the suggested human-likeness index.

In industrial settings, robotic tasks often require interaction with various objects, necessitating compliant manipulation to prevent damage while accurately tracking reference forces. Interaction controllers are typically employed for this purpose, but they need either human tinkering for parameter tuning or precise environmental modeling. Both these aspects can be problematic, as the former is a time-consuming procedure, and the latter is unavoidably affected by approximations, hence being prone to failure during the actual application. In order to address these challenges, current research focuses on designing high-performance force controllers. Along this line, this work introduces ORACLE ( O ptimized R esidual A ction for interaction C ontrol with L earned E nvironments), a novel force control approach. Exploiting neural networks, ORACLE predicts robot-environment interaction forces, which are then used in an optimal residual action controller to locally correct actions from a base force controller, minimizing the force-tracking error. Tested on a real Franka Emika Panda robot, ORACLE demonstrates superior force tracking performance compared to state-of-the-art controllers, with a short setup time.

In industrial settings, robotic tasks often require interaction with various objects, necessitating compliant manipulation to prevent damage while accurately tracking reference forces. Interaction controllers are typically employed for this purpose, but they need either human tinkering for parameter tuning or precise environmental modeling. Both these aspects can be problematic, as the former is a time-consuming procedure, and the latter is unavoidably affected by approximations, hence being prone to failure during the actual application. In order to address these challenges, the current research focuses on designing high-performance force controllers. Along this line, this work introduces optimized residual action for interaction control with learned environments (ORACLE), a novel force control approach. Exploiting neural networks, ORACLE predicts robot–environment interaction forces, which are then used in an optimal residual action controller to locally correct actions from a base force controller, minimizing the force-tracking error. Tested on a real Franka Emika Panda robot, ORACLE demonstrates superior force-tracking performance compared with the state-of-the-art controllers, with a short setup time.

Bolting operations are critical in industrial assembly and in the maintenance of scientific facilities, requiring high precision and robustness to faults. Although robotic solutions have the potential to improve operational safety and effectiveness, current systems still lack reliable autonomy and fault management capabilities. To address this gap, we propose a control framework for dependable robotized bolting tasks and instantiate it on a specific robotic system. The system features a control architecture ensuring accurate driving torque control and active compliance throughout the entire operation, enabling safe interaction even under fault conditions. By designing a multimodal human-robot interface (HRI) providing real-time visualization of relevant system information and supporting seamless transitions between automatic and manual control, we improve operator situation awareness and fault detection capabilities. A high-level supervisor (SV) coordinates the execution and manages transitions between control modes, ensuring consistency with the supervisory control (SVC) paradigm, while preserving the human operator's authority. The system is validated in a representative bolting operation involving pipe flange joining, under several fault conditions. The results demonstrate improved fault detection capabilities, enhanced operator situational awareness, and accurate and compliant execution of the bolting operation. However, they also reveal the limitations of relying on a single camera to achieve full situational awareness.

Space robotics poses unique challenges arising from the limitation of energy and computational resources, and the complexity of the environment and employed platforms. At the control center, offline motion planning is fundamental in the computation of optimized trajectories accounting for the system’s constraints. Smooth movements, collision and forbidden areas avoidance, target visibility (TV), and energy consumption are all important factors to consider to be able to generate feasible and optimal plans. When mobile manipulators (terrestrial and aerial) are employed, the base and the arm movements are often separately planned, ultimately resulting in suboptimal solutions. We propose an optimal whole body planner based on discrete dynamic programming and optimal interpolation. Kinematic redundancy is exploited for collision and forbidden areas avoidance, and to improve target illumination and visibility from onboard cameras. The planner, implemented in the Robot Operating System (ROS), interfaces the 3D Rover Operations Control Software (3DROCS), a mission planner used in several programs of the European Space Agency (ESA) to support planetary exploration surface missions and part of the ExoMars Rover’s planning software. The proposed approach is exercised on a simplified version of the Analog-1 Interact rover by ESA, a 7-degrees of freedom robotic arm mounted on a four wheels nonholonomic platform.

This addresses errors in [1]. Compared to the original article, we applied minor grammatical corrections and symbol refinements to clarify the adoption of the chosen notation and nomenclature.

This article presents the full dynamic model of the UR10 industrial robot. A triple-stage identification approach is adopted to estimate the manipulator's dynamic coefficients. First, linear parameters are computed using a standard linear regression algorithm. Subsequently, nonlinear friction parameters are estimated according to a sigmoidal model. Lastly, motor drive gains are devised to map estimated joint currents to torques. The overall identified model can be used for both control and planning purposes, as the accompanied robot operating system (ROS)2 software can be easily reconfigured to account for a generic payload. The estimated robot model is experimentally validated against a set of exciting trajectories and compared to the state-of-the-art model for the same manipulator, achieving higher current prediction accuracy (up to a factor of 4.43) and more precise motor gains. The related software is available at https://codeocean.com/capsule/8515919/tree/v2.

In this position paper we address the issue of acceptance in human-robot interaction, a topic often associated with anthropomorphism. The literature around this concept lacks of a discussion around the kinetic aspect of anthropomorphism, especially in applications where machine accuracy is required. We briefly summarize the results we achieved and outline some research directions promised to impact human-robot collaboration in sectors like manufacturing and medicine.