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Results show that the multi-agent design of RoboFind fits the demands of personalized object search, where verifying object identity before declaring completion is what makes the outcome something a user can rely on.
CoRef-GS is proposed, a cooperative referring Gaussian splatting framework that constructs local open-vocabulary instance-aware Gaussian maps, then aligns partially overlapping maps with a cross-agent alignment module by geometric and semantic consistency, and grounds queries using a view-conditioned mask relation graph.
INSPECT is introduced, which learns robot view preferences from records of a smart-glasses assistant that answers part queries and provides next-step guidance and achieves the highest view utility among the compared non-oracle policies and raises human-rated full verifiability.
PROVIA ranks mistakes best among the evaluated controlled baselines on CaptainCook4D, IndustReal, HoloAssist and IMPACT-ego, under a validation false-alarm budget and against controls that use timing alone.
Parallel execution of tasks in assistive robots can cut mission completion time by over 40%, revolutionizing how we think about robot coordination in dynamic environments.
X$^2$Localizer boosts single-frame retrieval performance by over 4% while maintaining full-video accuracy, bridging the gap between evaluation benchmarks and real-world applications.
