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About

Rosario Scalise is a robotics researcher whose work sits at the intersection of human-robot interaction, assistive technology, and autonomous manipulation. Their most significant contribution is leading the development of DROID, a large-scale in-the-wild robot manipulation dataset that has already garnered over 108 citations since its 2024 release, providing the community with critical training data for generalizable robotic skills. Scalise has made foundational contributions to robot-assisted feeding systems, notably through their influential 2020 paper "Is More Autonomy Always Better?" (69 citations), which challenged assumptions about full autonomy by demonstrating that effective assistive feeding must account for user preferences, impairment constraints, and real-world uncertainty. Their earlier work on natural language instructions for collaborative manipulation (2016, 35 citations) established key principles for how robots should interpret spatial references and perspective in human communication, while their 2018 dataset of 1,582 manipulation instructions remains a valuable resource for NLP researchers. Scalise’s research consistently bridges theoretical rigor with practical deployment, as evidenced by their 2025 lessons-learned paper on out-of-lab feeding systems, making them a leading voice in creating assistive robots that truly work for people with disabilities.