Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 1605.05120

Pushing the limits of the CyberGrasp for haptic rendering

Manuel Aiple·André Schiele
View original source ↗Read source document ↗Export metadata ↓

Abstract

The CyberGrasp is a well known dataglove-exoskeleton device combination that allows to render haptic feedback to the human fingers. Its design, however, restricts its usability for teleoperation through a limited control bandwidth and position sensor resolution. Therefore the system is restricted to low achievable contact stiffness and feedback gain magnitudes in haptic rendering. Moreover, the system prohibits simple adaption of its controller implementation. In this paper, the ExHand Box is presented, a newly designed back-end to widen the CyberGrasp's bandwidth restrictions and to open it up for fully customized controller implementations. The ExHand Box provides a new computer, interface electronics and motor controllers for the otherwise unmodified CyberGlove and CyberGrasp hand systems. The loop frequency of the new system can be freely varied up to 2 kHz and custom controllers can be implemented through an automatic code generation interface. System performance identification experiments are presented that demonstrate improved behavior in hard contact situations over a range of sampling periods. Maximum contact stiffnesses of up to 50kN/m in a stable condition are demonstrated, which is significantly higher than what could be achieved with the non-customized original system version. Moreover, a bilateral control experiment is conducted to demonstrate the new system's usability for generic teleoperation research. In this experiment a raycasting algorithm is introduced for pre-contact detection in order to compensate for high delay and jitter communication links between master and slave as they appear in an Ethernet network. It is demonstrated that the contact stiffness can be maintained in the order of magnitude of the system performance identification with a demonstrated stiffness of 41kN/m in a stable condition.

Explore related subjects

cs.HCcs.ROcs.SYeess.SY

Keep this discovery

BibTeXRIS

Manuel Aiple, André Schiele. 2016-05-17. Pushing the limits of the CyberGrasp for haptic rendering. https://doi.org/10.1109/icra.2013.6631073

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

ShellVis: Sandboxed Live Programming for Shell Scripts

Live programming provides visibility to programmers by running and tracing programs as they are edited. However, for programs with potentially harmful side effects, liveness can turn mistakes into disasters. We propose enabling live programming in environments with side effects via sandboxing: confining effects to a simulation of the true environment. We apply sandboxed live programming in the challenging context of shell scripting: a ubiquitous and powerful---yet notoriously opaque and error-prone---tool. ShellVis provides line-by-line feedback on a shell script's run-time behavior, with file operations sandboxed via a safe overlay of the file system. A qualitative user evaluation finds ShellVis to be helpful to participants, replacing tedious existing practices and instilling confidence. Participant responses also reveal areas for future research, particularly bridging the gulf of execution alongside the gulf of evaluation. ShellVis serves as a case study of how sandboxing can bring live-programming techniques into the many real-world programming contexts where side effects are important.

cs.HC↗
arXiv2026

Visual-Motion-Induced Modulation of Pedestrian Trajectories Using Spatially Distributed Multi-Display Signage in Public Spaces

Multi-display signage (MDS), now ubiquitous in urban environments, has the potential to influence human behavior and experience in public spaces. However, despite its unique capability to present spatially distributed dynamic visual stimuli, its current use is mainly limited to advertising. In this study, we propose a perception-based approach for laterally modulating pedestrian trajectories as a nonverbal means of guiding pedestrians in public spaces. The approach is motivated by vection, the illusion of self-motion, and uses laterally moving monochrome stripes, a standard stimulus in vection research, presented across spatially distributed displays to elicit postural responses that may bias pedestrian trajectories. We evaluated the approach through a controlled laboratory experiment and a real-world field deployment involving actual pedestrian flows in a national museum. The laboratory experiment examined whether the MDS setup induced trajectory shifts in the direction predicted by prior research on the behavioral effects of vection. The field deployment investigated whether comparable effects would emerge in aggregate pedestrian behavior during unconstrained movement under conditions closer to those of urban public spaces. In the laboratory, full-screen motion significantly biased walking trajectories in the direction of visual motion, whereas partial-stripe motion produced no significant directional effect. In the field deployment, opposing full-screen motion conditions produced direction-consistent differences in aggregate pedestrian positions. The field results, observed despite the substantial variability in real-world pedestrian flows, extend the controlled laboratory findings and provide ecologically valid evidence supporting practical MDS-based pedestrian modulation in public settings. The results further suggest that sufficient visual-motion coverage may be important.

cs.HC↗
arXiv2026

How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding

The utility of AI in multi-coder qualitative coding has been widely discussed, yet little empirical evidence exists to delineate the contexts in which it performs reliably. We address this gap by quantifying the effectiveness of multi-agent LLM coding across varied qualitative datasets, revealing key contextual and structural factors that mediate coding outcomes. We developed a literature-informed baseline pipeline that enables AI agents to independently code, debate, and reconcile disagreements. Results revealed that coding accuracy depends on factors such as codebook length, qualitative data similarity, and agent disagreement. Notably, intense and unresolved debates between agents led to higher accuracy. Our analysis showed that while LLMs emulate many human discussion behaviors, they lack adaptive responsiveness to context. From these findings, we offer design recommendations for building automated coding systems. Our open-source AI discussion dataset and methodological framework lay the groundwork for advancing the design of AI-mediated automated thematic analysis.

cs.HC↗