88,820 research outputs found
On interaction quality in human-robot interaction
In many complex robotics systems, interaction takes place in all directions between human, robot, and environment. Performance of such a system depends on this interaction, and a proper evaluation of a system must build on a proper modeling of interaction, a relevant set of performance metrics, and a methodology to combine metrics into a single performance value. In this paper, existing models of human-robot interaction are adapted to fit complex scenarios with one or several humans and robots. The interaction and the evaluation process is formalized, and a general method to fuse performance values over time and for several performance metrics is presented. The resulting value, denoted interaction quality, adds a dimension to ordinary performance metrics by being explicit about the interplay between performance metrics, and thereby provides a formal framework to understand, model, and address complex aspects of evaluation of human-robot interaction.Peer ReviewedPostprint (author's final draft
Getting to know Pepper : Effects of people’s awareness of a robot’s capabilities on their trust in the robot
© 2018 Association for Computing MachineryThis work investigates how human awareness about a social robot’s capabilities is related to trusting this robot to handle different tasks. We present a user study that relates knowledge on different quality levels to participant’s ratings of trust. Secondary school pupils were asked to rate their trust in the robot after three types of exposures: a video demonstration, a live interaction, and a programming task. The study revealed that the pupils’ trust is positively affected across different domains after each session, indicating that human users trust a robot more the more awareness about the robot they have
Trajectory Deformations from Physical Human-Robot Interaction
Robots are finding new applications where physical interaction with a human
is necessary: manufacturing, healthcare, and social tasks. Accordingly, the
field of physical human-robot interaction (pHRI) has leveraged impedance
control approaches, which support compliant interactions between human and
robot. However, a limitation of traditional impedance control is that---despite
provisions for the human to modify the robot's current trajectory---the human
cannot affect the robot's future desired trajectory through pHRI. In this
paper, we present an algorithm for physically interactive trajectory
deformations which, when combined with impedance control, allows the human to
modulate both the actual and desired trajectories of the robot. Unlike related
works, our method explicitly deforms the future desired trajectory based on
forces applied during pHRI, but does not require constant human guidance. We
present our approach and verify that this method is compatible with traditional
impedance control. Next, we use constrained optimization to derive the
deformation shape. Finally, we describe an algorithm for real time
implementation, and perform simulations to test the arbitration parameters.
Experimental results demonstrate reduction in the human's effort and
improvement in the movement quality when compared to pHRI with impedance
control alone
Evaluation of human-robot object co-manipulation under robot impedance control
The human-robot collaboration is a promising and challeng- ing field of robotics research. One of the main collaboration tasks is the object co-manipulation where the human and robot are in a continuous physical interaction and forces exerted must be handled. This involves some issues known in robotics as physical Human-Robot Interaction (pHRI), where human safety and interaction comfort are required. Moreover, a definition of interaction quality metrics would be relevant. In the current work, the assessment of Human-Robot object co-manipulation task was explored through the proposed metrics of interaction quality, based on human forces throughout the movement. This analysis is based on co-manipulation of objects with different dynamical properties (weight and inertia), with and without including these properties knowledge in the robot control law. Here, the human is a leader of task and the robot the follower without any information of the human trajectory and movement profile. For the robot control law, a well-known impedance control was applied on a 7-dof Kuka LBR iiwa 14 R820 robot. Results show that the consideration of object dynamical properties in the robot control law is crucial for a good and more comfortable interaction. Besides, human efforts are more significant with a higher no-considered weight, whereas it remains stable when these weights were considered
Human-robot coexistence and interaction in open industrial cells
Recent research results on human\u2013robot interaction and collaborative robotics are leaving behind the traditional paradigm of robots living in a separated space inside safety cages, allowing humans and robot to work together for completing an increasing number of complex industrial tasks. In this context, safety of the human operator is a main concern. In this paper, we present a framework for ensuring human safety in a robotic cell that allows human\u2013robot coexistence and dependable interaction. The framework is based on a layered control architecture that exploits an effective algorithm for online monitoring of relative human\u2013robot distance using depth sensors. This method allows to modify in real time the robot behavior depending on the user position, without limiting the operative robot workspace in a too conservative way. In order to guarantee redundancy and diversity at the safety level, additional certified laser scanners monitor human\u2013robot proximity in the cell and safe communication protocols and logical units are used for the smooth integration with an industrial software for safe low-level robot control. The implemented concept includes a smart human-machine interface to support in-process collaborative activities and for a contactless interaction with gesture recognition of operator commands. Coexistence and interaction are illustrated and tested in an industrial cell, in which a robot moves a tool that measures the quality of a polished metallic part while the operator performs a close evaluation of the same workpiece
Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs
Human-robot interaction will play an essential role in various industries and
daily tasks, enabling robots to effectively collaborate with humans and reduce
their physical workload. Most of the existing approaches for physical
human-robot interaction focus on collaboration between a human and a single
ground robot. In recent years, very little progress has been made in this
research area when considering aerial robots, which offer increased versatility
and mobility compared to their grounded counterparts. This paper proposes a
novel approach for safe human-robot collaborative transportation and
manipulation of a cable-suspended payload with multiple aerial robots. We
leverage the proposed method to enable smooth and intuitive interaction between
the transported objects and a human worker while considering safety constraints
during operations by exploiting the redundancy of the internal transportation
system. The key elements of our system are (a) a distributed payload external
wrench estimator that does not rely on any force sensor; (b) a 6D admittance
controller for human-aerial-robot collaborative transportation and
manipulation; (c) a safety-aware controller that exploits the internal system
redundancy to guarantee the execution of additional tasks devoted to preserving
the human or robot safety without affecting the payload trajectory tracking or
quality of interaction. We validate the approach through extensive simulation
and real-world experiments. These include as well the robot team assisting the
human in transporting and manipulating a load or the human helping the robot
team navigate the environment. To the best of our knowledge, this work is the
first to create an interactive and safety-aware approach for quadrotor teams
that physically collaborate with a human operator during transportation and
manipulation tasks.Comment: Guanrui Li and Xinyang Liu contributed equally to this pape
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