Flexible work is increasingly pursued as a means of achieving work-life balance, particularly as growing caregiving responsibilities for children and aging family members shape workers’ lives. Yet most HCI research has examined flexibility primarily through productivity and organizational perspectives, with less attention to how it intersects with workers’ personal and family responsibilities. To address this gap, we conducted a qualitative study with 20 workers in Singapore engaging in flexible arrangements to manage paid work and care responsibilities. Using an asset-based lens, we show that flexibility is not a static benefit but a continual practice of rhythm-making. Participants maintained rhythms by drawing on temporal and spatial assets, negotiated them through relational and institutional dynamics, and sustained them through intrapersonal assets such as self-care and positive reframing. Our study reframes blurred boundaries as resources rather than disruptions and offers design implications for technologies that support flexible workers’ everyday rhythm-making practices.

Understanding objects at the level of their constituent parts is fundamental to advancing computer vision, graphics, and robotics. While datasets like PartNet have driven progress in 3D part understanding, their reliance on untextured geometries and expert-dependent annotation limits scalability and usability. We introduce PartNeXt, a next-generation dataset addressing these gaps with over 23000 high-quality, textured 3D models annotated with fine-grained, hierarchical part labels across 50 categories. We benchmark PartNeXt on two tasks: (1) class-agnostic part segmentation, where state-of-the-art methods (e.g., PartField, SAMPart3D) struggle with fine-grained and leaf-level parts, and (2) 3D part-centric question answering, a new benchmark for 3D-LLMs that reveals significant gaps in open-vocabulary part grounding. Additionally, training Point-SAM on PartNeXt yields substantial gains over PartNet, underscoring the dataset’s superior quality and diversity. By combining scalable annotation, texture-aware labels, and multi-task evaluation, PartNeXt opens new avenues for research in structured 3D understanding.

This paper investigates two distinct paradigms for gaze-based control in human-robot collaboration (HRC). While gaze tracking offers a promising hands-free interaction method, the optimal mapping between eye movements and robot control remains an open research question. We examine two fundamentally different control approaches: (1) position-based control, which utilizes fiducial markers for spatial referencing and maps gaze positions directly to physical target locations; and (2) velocity-based control, whose functions are similar to a joystick where gaze position relative to camera frame centers determines movement direction and speed. Participants completed standardized pick-and-place tasks with both control methods. Performance was assessed through objective metrics including task completion time, trajectory efficiency, and error rates. Subjective experiences were evaluated using NASA Task Load Index questionnaires. Both systems incorporate a blink detection mechanism for gripper activation, enabling completely hands-free operation. This research addresses fundamental questions in eye-based robotic control for HRC, with applications spanning assistive technologies for mobility-impaired users, industrial settings that require hands-free operation, and medical environments where maintaining sterility is crucial. Results indicate significant differences between control paradigms, providing design insights for more intuitive and effective gaze-based interfaces in human-robot systems.