CORTEXA
← Browse
arxivcs.HCcs.RO2026-07-17

CASAband: Easy-to-Wear Textile Wristband using Shape Memory Alloy Actuators for Spatial and Temporal Haptic Feedback

Baekgyeom Kim, Anoush Sepehri, Jessica Healey, Taeuk Oh, Hyungseok Seo, Je-Sung Koh, Tania K. Morimoto

Haptic interfaces for the wrist and forearm offer an attractive alternative to hand-worn devices as they are simple to wear, leave the hands free for interaction with the real world, and interfere minimally with natural arm motions. To be useful in real-world settings, however, such devices must balance functionality, wearability and comfort, all while being fully untethered with minimal mass and volume. In this work, we present CASAband, a haptic wristband that integrates compliant amplified shape memory alloy actuators (CASA) into a multi-layered textile wristband to deliver spatial and temporal haptic feedback. CASAband operates completely untethered, generates no noise, and has a total mass of 63 g. The device incorporates four actuators that can generate up to 1.7 N of blocked force and 3.2 mm of free displacement with an operating bandwidth ranging from 1.34-6.59 Hz depending on the applied voltage. We conducted a perceptual study and determined that users could identify the location of a single haptic cue around their wrist and discriminate among several patterned cues with over 90% accuracy on average, highlighting that CASAband can be a suitable wearable interface to deliver information for real-world guidance and navigation tasks. To highlight the potential use cases for CASAband, we conducted two demonstrations: a pick and place task where the user relied only on haptic communication from a moderator, and an outdoor pedestrian navigation task where the user relied only on directional cues on the wrist. CASAband is one of the first haptic interfaces that balances the tradeoff between form and function and presents new opportunities for haptic feedback in the real world

View free PDFSource page

Related papers

arxivcs.ROcs.HC2026-07-31

STAGE: STyle-controllable Action GEneration for personalized autonomous driving

Zihao Liu, Xing Liu, Yizhai Zhang, Panfeng Huang

Driving style refers to the behavioral preferences that drivers maintain during driving, shaped by their diverse experiences, habits, and needs, and is typically reflected in varying levels of aggressiveness. If humans choose to use autonomous driving systems, they would expect t…

View free PDFSource page
arxivcs.ROcs.CVcs.HC2026-07-20

From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation

Nabeela Khan, Bowen Wu, Runwu Shi, Benjamin Yen, Takeshi Ashizawa, Carlos Toshinori Ishi, et al.

Recent sign language generation (SLG) systems increasingly output dense 3D body representations, which better preserve full-body kinematics and geometry for downstream embodiment on humanoid robots. However, these generated motions frequently exhibit self-intersections such as ha…

View free PDFSource page
arxivcs.ROcs.HCcs.LG2026-07-22

Towards Miniature Humanoid Tele-Loco-Manipulation Using Virtual Reality and Reinforcement Learning

Nicolas Kosanovic, Jordan Dowdy, Jean Chagas Vaz

Full-sized humanoid robot capabilities have grown exponentially in recent years, aiming towards general-purpose deployment in human environments. A popular control method used by manufacturers utilizes Virtual Reality for upper-body teleoperation and Reinforcement Learning for lo…

View free PDFSource page
arxivcs.ROcs.HCeess.SP2026-07-21

How defensive driving enhances driving safety: A driving simulator study on drivers' defensive driving behaviors

Xinzheng Wu, Junyi Chen, Shaolingfeng Ye, Yong Shen

Defensive driving is widely recognized as an advanced driving skill. However, whether and how defensive driving affects driving safety remains insufficiently investigated. This study examines the behavioral characteristics of defensive driving, its impact on driving safety, and t…

View free PDFSource page
arxivcs.RO2026-07-20

STeP: Signal Temporal Logic for Precise Specifications for Action Generation with Vision Language Models

Kasra Torshizi, Anukriti Singh, Sidharth Mathur, Khuzema Habib, Leo Du, Pratap Tokekar

Vision-language-action (VLA) models have shown impressive generalization, but often lack interpretability and can struggle to follow precise natural language instructions that encode spatial, temporal, and logical requirements. We propose a hierarchical framework that uses Signal…

View free PDFSource page