arXiv:2604.20967v2 Announce Type: replace
Abstract: Upper-limb exoskeletons (ULEs) have the potential to restore functional independence in individuals with severe motor impairments; however, the clinical relevance of wrist degrees of freedom (DoF), p…
arXiv:2604.28161v1 Announce Type: new
Abstract: The robotic manipulation of Deformable Linear Objects (DLOs) is a fundamental challenge due to the high-dimensional, non-linear dynamics of flexible structures and the complexity of maintaining topologic…
arXiv:2604.28192v1 Announce Type: new
Abstract: Vision-Language-Action (VLA) models have increasingly incorporated reasoning mechanisms for complex robotic manipulation. However, existing approaches share a critical limitation: whether employing expli…
arXiv:2604.26988v1 Announce Type: new
Abstract: Current robots are capable of computing plans to accomplish complex tasks. However, real-world environments are inherently open and dynamic, and unforeseen situations frequently arise during plan executi…
arXiv:2502.14270v3 Announce Type: replace
Abstract: Birth weight serves as a fundamental indicator of neonatal health, closely linked to both early medical interventions and long-term developmental risks. Traditional predictive models, often constrain…
arXiv:2604.28010v1 Announce Type: new
Abstract: We reframe clinician overrides of clinical AI recommendations as implicit preference data – the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annot…
arXiv:2604.27804v1 Announce Type: cross
Abstract: The rapid proliferation of image generation models and other artificial intelligence (AI) systems has intensified concerns regarding data privacy and user consent. As the availability of public dataset…
arXiv:2604.27673v1 Announce Type: cross
Abstract: We introduce Target-Event-Agent Networks (TEA Nets) as a computational framework to extract subjects (“Agents”), verbs (“Events”), and objects (“Targets”) from texts. Grounded in cognitive network s…
arXiv:2604.27014v1 Announce Type: new
Abstract: The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learning models. Privacy regulations restrict data sharing, …
arXiv:2604.28024v1 Announce Type: new
Abstract: Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw dat…