1,721,156 research outputs found
Human-oriented approaches for assistive and rehabilitation robotics - Engineering methods, technical implementation, and treatment
No full textUnal, Ramazan/0000-0002-2129-797X; Salvietti, Gionata/0000-0001-9170-4051; Mastrogiovanni, Fulvio/0000-0001-5913-1898; Beckerle, Philipp/0000-0001-5703-602
ASDF Evaluation Dataset + Corner Clamp Base
No full text<p>Evaluation data for ASDF and Corner Clamp Base Training Data</p>
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<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
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Mechatronic designs for a robotic hand to explore human body experience and sensory-motor skills: a Delphi study
Full text linkTo bridge the gap between users' expectations and technological solutions, a better understanding of human body experience and sensory-motor skills is mandatory. This could pave the way towards a novel generation of robotic hands, which can be successfully employed in everyday life e.g. in prosthetics and assistive robotics. Available robotic hands are still far from matching the requirements of the corresponding experimental and real-world applications, e.g. fast motions might be achieved at the expense of accuracy. Knowledge of the users' sensory-motor skills can guide technical developments, e.g. prosthetic design processes. This paper presents design solutions developed in a Delphi study. Explorative questionnaires are prepared to acquire and elaborate expert opinions to improve the design of previously developed robotic anthropomorphic hands. By gathering and fusing expert opinions, novel robotic hand and wrist concepts specifically optimized regarding body experience and sensory-motor skill research are developed. In three rounds, experts with experience in robotic hand design and/or control analyze, develop, and rank solutions for mechanisms, actuators, and control, which result in overall design concepts. The technical concepts and implications resulting from the study are discussed considering psychological and biomechanical aspects
Can wearable haptic devices foster the embodiment of virtual limbs?
Full text linkIncreasing presence is one of the primary goals of virtual reality research. A crucial aspect is that users are capable of distinguishing their self from the external virtual world. The hypothesis we investigate is that wearable haptics play an important role in the body experience and could thereby contribute to the immersion of the user in the virtual environment. A within-subject study (n=32) comparing the embodiment of a virtual hand with different implementations of haptic feedback (force feedback, vibrotactile feedback, and no haptic feedback) is presented. Participants wore a glove with haptic feedback devices at thumb and index finger. They were asked to put virtual cubes on a moving virtual target. Touching a virtual object caused vibrotactile-feedback, force-feedback or no feedback depending on the condition. These conditions were provided both synchronously and asynchronously. Embodiment was assessed quantitatively with the proprioceptive drift and subjectively via a questionnaire. Results show that haptic feedback significantly improves the subjective embodiment of a virtual hand and that force feedback leads to stronger responses to certain subscales of subjective embodiment. These outcomes are useful guidelines for wearable haptic designer and represent a basis for further research concerning human body experience, in reality, and in virtual environments
In-Hand Manipulation with Synergistic Actuated Robotic Hands: An MPC-Based Approach
No full textIn-hand manipulation, or dexterous manipulation, is one of the most complex challenges in robotics as it requires the accurate coordination of multiple degrees of freedom. While several solutions have been presented for fully actuated hands, less work has focused on underactuated grippers. Synergies can be interpreted as a method for coupling joint motions, constraining the hand's degrees of freedom, and thereby reducing the number of control inputs. In this paper, we propose a model predictive control scheme (MPC) that integrates synergies to implement dexterous in-hand manipulation with robotic hands. In the MPC formulation, synergies can either be considered as constraints on the joint variables or are directly inserted in the system function with a reallocation of the input variables acting on the joints. Through several sets of simulations we compare these two approaches and show their main features
How Positioning Wearable Haptic Interfaces on Limbs Influences Virtual Embodiment
No full textWith increasing use of computer applications and robotic devices in our everyday life, and with the advent of metaverse, there is an urgent need of developing new types of interfaces that facilitate a more intuitive interaction in physical and virtual space. In this work, we investigate the influence of the location of haptic feedback devices on embodiment of virtual hands and user load during an interactive pick-and-place task. To do this, we conducted a user study with a 3x2 repeated measure experiment design: feedback position is varied between the distal phalanx of the index finger and the thumb, the proximal phalanx of the index finger and the thumb, and the wrist. These conditions of feedback are tested with the stimuli applied synchronously to the participant in one case, and with an additional delay of 350 ms in the second case. The results show that the location of the haptic feedback device does not affect embodiment, whereas the delay, i.e., whether the feedback is applied synchronously or asynchronously, affects embodiment. This suggests that for pick-and-place tasks, haptic feedback devices can be placed on the user's wrist without compromising performance making the hands to remain free, allowing unobstructed hand visibility for precise motion tracking, thereby improving accuracy
ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation - Training Set - Corner Clamp Part 1
No full text<p>@article{schieber2024asdf,<br> title={ASDF: Assembly State Detection Utilizing Late Fusion by Integrating 6D Pose Estimation},<br> author={Schieber, Hannah and Li, Shiyu and Corell, Niklas and Beckerle, Philipp and Kreimeier, Julian and Roth, Daniel},<br> journal={arXiv preprint arXiv:2403.16400},<br> year={2024}<br>}</p>
Nutzendenerfahrung erfassen und verstehen zur Verbesserung der Mensch-Roboter-Interaktion
No full textAs robotic systems become increasingly integrated into everyday life, improving the user experience in human-robot interaction can facilitate their acceptance and long-term use.
This thesis examines how trust and embodiment, two key aspects of the user experience, influence non-expert users in two domains: adaptive collaboration with a robotic arm and embodiment in the robotic limb illusion. These factors are critical for user acceptance, shaping how users interact with robotic systems.
The first part investigates trust in adaptive collaborative robots. One approach to enhancing trust is human-centered reinforcement learning, particularly learning from demonstration, where users teach robots new tasks through direct interaction. Two user studies explore how adding human training data to reinforcement learning agents affects trust. The first study examines whether human-trained agents influence user trust, while the second extends this to a physical robotic arm, where participants train the robot themselves. Results show that incorporating expert human data enhances trust, even when performance improvements are not statistically significant.
The second part focuses on embodiment in the robotic limb illusion, building on research from the rubber hand illusion. Bayesian causal inference models are applied to analyze embodiment, first by fitting a model to robotic leg illusion data, showing that visual priors play a crucial role in multisensory integration. A follow-up user study gathers sufficient data for individual-level analysis, allowing the model to be adapted for personalized embodiment responses. By estimating user-specific priors, this study provides insights into individual embodiment differences and supports the design of personalized prosthetic and robotic augmentation devices.
Together, these studies contribute to human-centered human-robot interaction research by demonstrating how trust and embodiment can be systematically influenced through human data integration and cognitive modeling. The findings suggest that (1) trust in collaborative robots should be considered separately from task performance and (2) personalized embodiment models could enhance prosthetic and robotic limb integration. By addressing these challenges, this thesis advances the development of adaptive, user-friendly robotic systems, ensuring they align more closely with human needs and expectations
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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