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Proactive anomaly anticipation in contact-rich manipulation is achieved by predicting future physical interactions, enabling earlier detection and improved task success.
NGM-RAG achieves superior performance in multi-hop reasoning by leveraging graph structures, outperforming traditional RAG methods and state-of-the-art graph-enhanced models.
HALOMI bridges the human-to-humanoid gap, achieving 85% success in complex loco-manipulation tasks by leveraging active perception from human demonstrations.