1,720,998 research outputs found
INTELLIMAN. WP4. Adaptive shared autonomy. T4_2. Advanced human-robot interaction modalities. Robotic endoscope. v0
No full textThe dataset is related to results of two key aspects of endoscope performance: tissue interaction and stiffness variation. Through a series of controlled experiments, the endoscope’s ability to interact with mock biological tissues is assessed, demonstrating successful force application using both agonistonly and antagonistic functioning modalities. Furthermore, the endoscope’s resilience to external disturbances is evaluated, with results showing significant improvements in stiffness and response to perturbations when utilizing antagonistic control. The data were produced in the framework of Horizon Europe INTELLIMAN project and are presented in the publication:
E. Fratarcangeli et al., "Additively Manufactured Flexible Endoscope Driven By Guided Antagonistic Twisted String Actuation: A Pilot Experimental Evaluation," 2024 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), Boston, MA, USA, 2024, pp. 867-872, doi: 10.1109/AIM55361.2024.10637078
INTELLIMAN. WP4. Adaptive shared autonomy. T4_2. Advanced human-robot interaction modalities. ErRP. v0
No full textError-Related Potentials (ErRPs) in brain activity can provide insight into user intent prediction errors during robot teleoperation. This dataset was collected following a protocol designed to systematically acquire electroencephalography (EEG) signals while participants performed simplified virtual teleoperation and telemanipulation tasks of increasing cognitive load. Artificially induced error trials were included to enable systematic study of error responses. With synchronized triggers and command logs, the dataset enables the analysis of how cognitive load affects the occurrence, amplitude, and timing of ErRPs, supporting research in adaptive human-robot interaction. A preliminary dataset with recordings from 5 subjects is made available to the community. The data were produced in the framework of Horizon Europe INTELLIMAN project
INTELLIMAN. WP4. Adaptive shared autonomy. T4_3. Human intent detection for autonomy arbitration. Self supervised myocontrol. v0
No full textThe dataset is related a novel Human-Robot interface (HRi) based on self-supervised regression of sEMG signals, combining Non-Negative Matrix Factorization (NMF) with Deep Neural Networks (DNN) in order to both avoid explicit labeling procedures and have powerful nonlinear fitting capabilities. Experiments involving 10 healthy subjects were carried out assessing real-time control of a wearable anthropomorphic robot hand. The data were produced in the framework of Horizon Europe INTELLIMAN project and are presented in the publication:
R. Meattini, A. Caporali, A. Bernardini, G. Palli and C. Melchiorri, "Self-Supervised Regression of sEMG Signals Combining Non-Negative Matrix Factorization With Deep Neural Networks for Robot Hand Multiple Grasping Motion Control," in IEEE Robotics and Automation Letters, vol. 8, no. 12, pp. 8533-8540, Dec. 2023, doi: 10.1109/LRA.2023.3329764
REMODEL. WP3. User And System Interface. T3_8. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Hand motion mapping methods review. v0
No full textThe dataset is related to the approaches proposed in the literature to address the problem of mapping human to robot hand motions are summarized and discussed, organized under macrocategories related to the great quantity of presented methods that are often difficult to be seen from a general point of view due to different fields of application, specific use of algorithms, terminology, and declared goals of the mappings. The work mainly focuses on the following six categories: direct joint, direct Cartesian, task-oriented, dimensionality reduction based, pose recognition based, and hybrid mappings. The data are presented in the publication:
R. Meattini, R. Suárez, G. Palli and C. Melchiorri, "Human to Robot Hand Motion Mapping Methods: Review and Classification," in IEEE Transactions on Robotics, 2022, doi: 10.1109/TRO.2022.3205510
REMODEL. WP3. User And System Interface. T3_6. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Simulative evaluation of hand motion mapping. v0
No full textThe dataset contains the data related to human to robot hand mapping, ensuring natural motions and predictability for the operator, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. We exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We consider two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The data are presented in the publication:
Meattini, R., Chiaravalli, D., Palli, G., & Melchiorri, C. (2022). Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information. Frontiers in Robotics and AI, 9:878364. doi: 10.3389/frobt.2022.87836
REMODEL. WP3. User And System Interface. T3_4. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Augmented Kinesthetic Teaching. v0
No full textThe datasets contain the data related to an augmented kinesthetic teaching system, which is based on surface electromyographic (sEMG) measurements from the operator forearm. Specifically, sEMG signals are used for minimal-training unsupervised estimation of forearm's muscles co-contraction level. In this way, also exploiting a vibrotactile bio-feedback, we evaluate the ability of operators in stiffening their hand - during kinesthetic teaching - in order to modulate the estimated level of muscle co-contraction to (i) match target levels and (ii) command the opening/closing of a gripper, i.e. in exploiting their sEMG signals for effective augmented robot kinesthetic teaching tasks. The data are related to the publication:
R. Meattini, D. Chiaravalli, L. Biagiotti, G. Palli and C. Melchiorri, "Combining Unsupervised Muscle Co-Contraction Estimation With Bio-Feedback Allows Augmented Kinesthetic Teaching," in IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 6180-6187, Oct. 2021, doi: 10.1109/LRA.2021.3092269
REMODEL. WP6. Sensory Systems And Mechatronic Tools. T6_2. Evaluation of a deformable skin tactile sensor. v0
No full textThe dataset contains the data related to three different types of data acquisitions, on which we trained and tested an artificial neural network (ANN). The procedure for the training and testing of the ANN is realized for each combination of inflated air and vertical force levels, by means of a nested cross-validation (CV). In detail, the CV is composed by two nested loops. The first data acquisition is composed by the output of the Inertial Measurement Unit (IMU) while the robotic manipulator UR5 is pressing on its surface with a metal stick end-effector on a grid on 42 different locations (namely: the 42-locations-session); the data acquired during this process from the tactile sensor are labeled based on the Cartesian position of the robot, therefore associating the signals with 42 different classes. The second data acquisition is related to the IMU data when the robot is pressing on the tactile sensor by means of a linear-like end-effector, applying the orientations of 0o, 30o, 60o, 90o, 120o and 150o (namely: the 6-orientations-session); in this case, the signals are labeled according to 6 classes, that corresponds to the six orientations of the linear region of contact points. Finally, the third data acquisition is built in the same way of the second, but considering the orientations of the linear region of contact points related to 0o, 45o, 90o and 135o (namely: the 4-orientations-session), corresponding to the labeling of the signals according to 4 classes. For each type of data acquisition, we repeated the experiment two times, and, for each of this repetition, we acquired the data for 3 levels of vertical force applied on the tactile sensor – 0.5 N, 1 N and 2 N (using the information from the force sensor at the base of the tactile sensor) – and 3 levels of inflating air – 5 ml, 7 ml and 10 ml (measured by using a syringe). In this way, we obtained a total amount of 54 datasets (27 datasets for the first session, and 27 datasets for the second session.) The data is related to the publication:
Y. Iwamoto, R. Meattini, D. Chiaravalli, G. Palli, K. Shibuya and C. Melchiorri, "A Low Cost Tactile Sensor for Large Surfaces Based on Deformable Skin with Embedded IMU," 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Tampere, Finland, 2020, pp. 501-506, doi: 10.1109/ICPS48405.2020.9274737
REMODEL. WP3. User And System Interface. T3_3. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Evaluation of physical human-robot interaction modalities. v0
No full textThe datasets contain the data related to the experiment was carried out involving four subjects – named U1, U2, U3, U4 – in a series of physical and muscle strength training tasks, related to the publication:
R. Meattini, D. Chiaravalli, G. Palli and C. Melchiorri, "sEMG-Based Human-in-theLoop Control of Elbow Assistive Robots for Physical Tasks and Muscle Strength Training," in IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5795-5802, Oct. 2020. (DOI: 10.1109/LRA.2020.3010741
REMODEL. WP3. User And System Interface. T3_5. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Teleoperation interface. v0
No full textThe datasets contain the data related to the first stage implementation of a haptic device towards a complete 3-D workspace twisted-string actuated haptic interface. In particular, a 2-D setup is presented, with the aim of preliminarly testing the behaviour of this novel haptic system, especially with respect to the adopted cable-based actuation solution. In particular, the component descriptions, kinematics of the planar device and the controller for teleoperation purposes are considered. The data are discussed in the following publication:
L. Feenstra et al., "Towards a Twisted String Actuated Haptic Device: Experimental Testing of a 2-D Virtual Environment and Teleoperation Interface," 2021 20th International Conference on Advanced Robotics (ICAR), 2021, pp. 757-762, doi: 10.1109/ICAR53236.2021.9659420
REMODEL. WP3. User And System Interface. T3_3. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Human to robot hand motion mapping method. v0;
No full textThe datasets contain the data related to a novel hybrid approach that combines both joint and Cartesian mappings in a single solution. In particular, we exploit the a priori, in-hand information related to the areas of the workspace in which thumb and finger fingertips can get in contact. This allows to define, for each finger, a zone of transition from joint to Cartesian mapping. As a consequence, both hand shape during volar grasps and correctness of the fingertip positions for precision grasps are preserved, despite the master-slave kinematic dissimilarities. The data are related to the publication:
R. Meattini, D. Chiaravalli, G. Palli and C. Melchiorri, "Exploiting In-Hand Knowledge in Hybrid Joint-Cartesian Mapping for Anthropomorphic Robotic Hands," in IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5517-5524, July 2021, doi: 10.1109/LRA.2021.3078658
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