Dong Li

Winning the IROS 2026 Indoor Exploration Competition, this solution integrates pre-trained map completion and time-aware homing for single-robot exploration, and a utility-driven, data-sharing approach for multi-robot coordination.

It is found that all linear performance leaders effectively add only a nuclear-norm based regularizer, or a Frobenius-norm based regularizer, or a Frobenius-norm based regularizer.

Achieving precise concept removal in text-to-video models without sacrificing generation quality could redefine safety standards in generative AI.

CATeye reveals that effectively isolating invariant attributes and edges can lead to substantial improvements in detecting evolving fraud patterns in e-commerce.

Query-aware evidence forests can drastically reduce memory lifecycle costs while improving accuracy, setting a new standard for multimodal agent memory management.

Allocating additional computation during decision-making boosts long-horizon robot manipulation success by significantly enhancing subtask prediction accuracy.