EmPRISE Lab me-retweet
Physical caregiving is one of robotics' hardest frontiers: it is contact-rich, physically intensive, long-horizon, safety-critical, and full of deformable objects.
Physical caregiving tasks such as bathing, dressing, transferring, toileting, and grooming require professional training and considerable practical experience. Yet, no existing dataset captures how expert caregivers perceive, interact, and adapt in real-time when performing these tasks, in a form that robots can learn from.
✨ We introduce OpenRoboCare at #IROS2025, the first expert-collected, multi-task, multimodal dataset for physical robot caregiving, featuring:
🩺 21 expert occupational therapists demonstrating caregiving procedures
🛠️ 15 caregiving tasks across 5 Activities of Daily Living (bathing, dressing, transferring, toileting, grooming)
🧍 2 hospital-grade manikins for safety and repeatability
🎥 5 synchronized sensing modalities: RGB-D, pose tracking, eye gaze, tactile sensing, and expert task & action annotations
📂 315 sessions · 19.8 hrs · 31,185 samples
Beyond raw data, OpenRoboCare distills core physical caregiving insights:
- 3 core principles followed by occupational therapists: pre-positioning, anticipation of body mechanics, and task efficiency.
- 4 key physical techniques: the bridge strategy, segmental rolling, wheelchair recline, and stabilization of key control points.
- Quantitative patterns in task duration, predictive gaze behavior that precedes physical contact, and the timing, magnitude, and spatial distribution of contact forces across body regions and task phases.
The dataset will be made openly accessible through the AWS Open Data Sponsorship Program soon.
🌐 Check out our project website for more visuals and insights: emprise.cs.cornell.edu/robo-care/
This work is led by: @xiaoyul14, @RealZiangLiu, and Kelvin Lin. This is a collaboration with Harold Soh's group from NUS and @DimitropoulouDr from CUIMC.
@EmpriseLab @Cornell_CS @IROS2025 @awscloud
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