CORTEXA
← Browse
arxivcs.AIcs.CRcs.HCcs.RO2026-07-08

Idiobionics: The Unification of Privacy and Intelligent Robotic Prostheses

Kwesi Afari Darfoor, Patrick M. Pilarski, Bailey Kacsmar

The human body is at the center of a growing family of technologies designed to tightly and persistently couple biological and digital systems. Robotic prostheses are a representative example of this tight coupling. Also referred to as bionic limbs, robotic prostheses are devices that support people who have lost limbs in pursuing daily life activities such as walking and grasping objects. Bionic limbs are now perceptive and responsive owing to their integration with advanced sensors and artificial intelligence-based control approaches. Consequently, such robotic prostheses can now be viewed as semiautonomous wearable robotic systems that can co-adapt with their users. However, the same sensing and control advancements that increase the capability of robotic prostheses also introduce threat vectors that could be exploited by malicious entities to violate the privacy of users. To fully realize the benefits of next-generation bionic limbs, we maintain it is important to directly understand and address these privacy risks and the barriers they might present to user adoption. This paper therefore introduces a new line of inquiry we term idiobionics to holistically investigate issues at the intersection of privacy and intelligent bionic limbs. As the main contribution of this paper, we define idiobionics, ground it in related literature, and provide preliminary evidence showing and discussing potential adversarial attacks that could exploit intelligent bionic limb designs. We then contribute a curated list of open research questions within idiobionics that are relevant to researchers in wearable robotics and other human-facing autonomous systems. We expect that idiobionics research will help unlock the full potential of robotic prostheses and related bionic devices.

View free PDFSource page

Related papers

arxivcs.ROcs.AIcs.HCcs.LG2026-07-08

Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report

Xufeng Zhao, Fuzhi Yang, Jianhui Chen, Li Gao, Zhang Meng, Jie Gao, et al.

The motion controller is one of the most fundamental modules in embodied intelligence systems. Driven by large-scale human motion-capture data and the motion-tracking paradigm, humanoid control has achieved remarkable progress in recent years. However, migrating this recipe to th…

View free PDFSource page
arxivcs.ROcs.AIcs.HC2026-07-07

Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction

Antonio Andriella, Jauwairia Nasir, Andrea Rezzani, Alyssa Kubota, Dimitri Lacroix, Tamlin Love, et al.

While personalisation is becoming a defining capability in human-robot interaction (HRI), the existing literature on responsible personalisation remains fragmented, offering isolated accounts of ethical risks without a structured understanding of how they emerge across interactio…

View free PDFSource page
arxivcs.ROcs.AIcs.CRcs.LG2026-07-20

Reasoning as a Double-Edged Sword: Architecture and Cross-Stage Robustness in Vision-Language-Action Models

Tuan Duong Trinh, Naveed Akhtar, Basim Azam

Does adding a reasoning step make a Vision-Language-Action (VLA) model more robust to perturbation? Intuitively, a policy that reasons before acting should absorb a perturbed input better than one that maps observations directly to actions. We test this premise head-on across thr…

View free PDFSource page
arxivcs.CRcs.AIcs.ROeess.SY2026-07-20

RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control

Di Lu, Bo Zhang, Xiyuan Li, Yongzhi Liao, Xuewen Dong, Yulong Shen, et al.

Natural-language control offers a promising interface for unmanned aerial vehicles (UAVs), but directly applying self-hosted computer-use agents (SHCUAs) to UAV control introduces a structural mismatch. SHCUAs are designed for interactive host-side tool use, where delayed agent i…

View free PDFSource page
arxivcs.CVcs.AIcs.GRcs.HCcs.RO2026-07-17

EgoExoMoCap: Distributed Ego-Exo Human Motion Capture

Jiaxi Jiang, Bharat Lal Bhatnagar, Nan Yang, Lingni Ma, Sebastian Starke, Robin Kips, et al.

Human motion capture from head-mounted devices (HMDs) offers a scalable way to acquire real-world human motion and interaction data, which is crucial for applications in embodied AI and VR/AR. Existing approaches focus on either egocentric body tracking, estimating the motion of…

View free PDFSource page