169,956 research outputs found
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
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_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. 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
REMODEL. WP3. User And System Interface. T3_8. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Robot programming by demonstration. v0
No full textThis dataset was generated in the framework of the Horizon 2020 project REMODEL. The dataset is related to enhancing existing Programming by Demonstration approaches with an additional input channel, the hand stiffness, that the operator continuously modulates during the demonstration, estimated from the forearm surface electromyography and translated into a request for a higher or lower accuracy level. It includes experimental data collected with sEMG sensors and robot intrinsic sensors during two experimental sessions in which human subjects have led a robotic arm to perform specific tasks. The data are presented in the publication:
L. Biagiotti, R. Meattini, D. Chiaravalli, G. Palli and C. Melchiorri, "Robot Programming by Demonstration: Trajectory Learning Enhanced by sEMG-Based User Hand Stiffness Estimation," in IEEE Transactions on Robotics, vol. 39, no. 4, pp. 3259-3278, Aug. 2023, doi: 10.1109/TRO.2023.3258669
REMODEL. WP3. User And System Interface. T3_7. Teaching By Demonstration Of Skills For New Assembly References And Tasks. sEMG based regression of hand grasping motions. v0
No full textThe dataset contain the data related to a novel sEMG-based minimally supervised regression approach capable of performing nonlinear fitting without the necessity for point-by-point training data labelling, exploiting a differentiable version of the Dynamic Time Warping (DTW) similarity – referred to as soft-DTW divergence – as loss function for a flexible neural network architecture. This is a different paradigm with respect to state-of-the-art approaches in which sEMG-based control of robot hands is mainly realized using supervised or unsupervised machine learning based regression. The data are presented in the publication:
R. Meattini, A. Bernardini, G. Palli and C. Melchiorri, "sEMG-Based Minimally Supervised Regression Using Soft-DTW Neural Networks for Robot Hand Grasping Control," in IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10144-10151, Oct. 2022, doi: 10.1109/LRA.2022.3193247
REMODEL. WP3. User And System Interface. T3_8. Teaching By Demonstration Of Skills For New Assembly References And Tasks. Intuitive robot programming. v0
No full textThis dataset was created in the framework of the Horizon 2020 project REMODEL, and is related to a robot programming framework for the simultaneous teaching of both trajectories by means of kinesthetic teaching, and robot interaction behavior by means of impedance shaping along the trajectory exploiting a wearable interface. Specifically, the wearable interface is designed to not affect the free motion of the operator, necessary to perform kinesthetic teaching, and it is based on the usage of surface electromyography (sEMG) and vibrotactile stimulation. In the experiment, the programming of requested compliance levels during the kinesthetic teaching of a trajectory is performed, and the reported results show that the provided wearable interface is successfully exploited by the operator. The data are presented in the publication:
Meattini, R., Chiaravalli, D., Galassi, K., Palli, G., & Melchiorri, C. (2022). Experimental Evaluation Of Intuitive Programming Of Robot Interaction Behaviour During Kinesthetic Teaching Using sEMG And Cutaneous Feedback. IFAC-PapersOnLine, 55(38), 1-6. https://doi.org/10.1016/j.ifacol.2023.01.12
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
Personalization in the Interactive EPUB 3 Reading Experience
No full textIn this paper, we describe the study conducted to investigate accessibility using EPUB 3 with particular focus on interaction via screen reader. A multimedia and interactive EPUB 3 prototype was designed for the purpose. In particular, personalization features based on user preferences were designed to customize the reading experience and enrich the interactive experience. Despite the fact that the EPUB format is based on HTML5, and numerous guidelines for web-based technology can be applied to overcome accessibility barriers, several issues still exist with the current standard EPUB 3 when accessing via screen reader. This study contributes to digital publishing for assistive technology and reading application development by promoting accessibility in EPUB interaction. Thus, some considerations and suggestions in that direction end the paper
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