People living with dementia (PLWDs) face progressively declining abilities in medication management-from simple forgetfulness to complete task breakdown-yet most assistive technologies fail to adapt to these changing needs. This one-size-fits-all approach undermines autonomy, accelerates dependence, and increases caregiver burden. Occupational therapy principles emphasize matching assistance levels to individual capabilities: minimal reminders for those who merely forget, spatial guidance for those who misplace items, and comprehensive multimodal support for those requiring step-by-step instruction. However, existing robotic systems lack this adaptive, graduated response framework essential for maintaining PLWD independence. We present an adaptive multimodal robotic framework using the Pepper robot that dynamically adjusts assistance based on real-time assessment of user needs. Our system implements a hierarchical intervention model progressing from (1) simple verbal reminders, to (2) verbal + gestural cues, to (3) full multimodal guidance combining physical navigation to medication locations with step-by-step verbal and gestural instructions. Powered by LLM-driven interaction strategies and multimodal sensing, the system continuously evaluates task states to provide just-enough assistance-preserving autonomy while ensuring medication adherence. We conducted a preliminary study with healthy adults and dementia care stakeholders in a controlled lab setting, evaluating the system's usability, comprehensibility, and appropriateness of adaptive feedback mechanisms. This work contributes: (1) a theoretically grounded adaptive assistance framework translating occupational therapy principles into HRI design, (2) a multimodal robotic implementation that preserves PLWD dignity through graduated support, and (3) empirical insights into stakeholder perceptions of adaptive robotic care.

Specialized machine learning (ML) models tailored to users needs and requests are increasingly being deployed on smart devices with cameras, to provide personalized intelligent services taking advantage of camera data. However, two primary challenges hinder the training of such models: the lack of publicly available labeled data suitable for specialized tasks and the inaccessibility of labeled private data due to concerns about user privacy. To address these challenges, we propose a novel system SpinML, where the server generates customized Synthetic image data to Privately traIN a specialized ML model tailored to the user request, with the usage of only a few sanitized reference images from the user. SpinML offers users fine-grained, object-level control over the reference images, which allows user to trade between the privacy and utility of the generated synthetic data according to their privacy preferences. Through experiments on three specialized model training tasks, we demonstrate that our proposed system can enhance the performance of specialized models without compromising users privacy preferences.

Nimble enough to remove a canister from a cinderblock. The ways robots progress don't always mirror the stages of childhood development, but it's eerie when they do. We've seen robots stumble to walk, watched as they learned the basics of language, and now we can see one dexterous enough to move color disks from one peg to another, stacking them in the correct order. It can also do more complex tasks, like opening safety lids on pill bottles. Aptly named the "Highly Dexterous Manipulation System", the robot was developed by Resquared Robotics, with funding from the Army and Navy.