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x-humanoid

Industry researcheurope · fr
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Research library20linked papers
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Selected work

Representative Papers

HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling

Oct 08, 2026

This study addresses the challenge of capturing synergistic information in multimodal learning by proposing the HRIL framework. It reveals that synergy originates from higher-order statistical dependencies and explicitly models the multimodal joint distribution through empirical cross-moment tensor construction and Tucker decomposition. Furthermore, a synergy-aware regularizer integrated with self-supervised contrastive learning is designed to prevent energy concentration and preserve higher-order coupling capabilities. Experimental results demonstrate that the proposed method outperforms existing approaches on both controlled tasks and real-world benchmarks, significantly enhancing model performance in scenarios dominated by synergistic interactions.

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Humanoid Horizon: Extending Task Horizon in Whole-Body Loco-Manipulation via Parallel Training, Dynamic Starting, and Reward Gating

Oct 06, 2026

This study addresses the performance bottlenecks in long-horizon whole-body mobile manipulation for humanoid robots caused by reward bias and catastrophic forgetting. To this end, we propose a unified policy framework that introduces parallel streaming training to eliminate sequential dependencies, dynamic initialization to enhance phase-boundary robustness, and a reward gating mechanism to prevent interference with previously placed objects. Furthermore, multi-scenario reinforcement learning is synergistically optimized via a shared policy network. Experimental results on the LHM-Humanoid benchmark demonstrate that our approach achieves single-phase success rates exceeding 80% and significantly outperforms baselines on long-sequence tasks with graceful performance degradation. Overall, the proposed method effectively improves generalization and stability in complex manipulation scenarios.

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Recent publications

Latest Papers

HRIL: Learning Multimodal Synergy via Higher-Order Tensor Modeling

Oct 08, 2026

This study addresses the challenge of capturing synergistic information in multimodal learning by proposing the HRIL framework. It reveals that synergy originates from higher-order statistical dependencies and explicitly models the multimodal joint distribution through empirical cross-moment tensor construction and Tucker decomposition. Furthermore, a synergy-aware regularizer integrated with self-supervised contrastive learning is designed to prevent energy concentration and preserve higher-order coupling capabilities. Experimental results demonstrate that the proposed method outperforms existing approaches on both controlled tasks and real-world benchmarks, significantly enhancing model performance in scenarios dominated by synergistic interactions.

0 citationsRead paper

Humanoid Horizon: Extending Task Horizon in Whole-Body Loco-Manipulation via Parallel Training, Dynamic Starting, and Reward Gating

Oct 06, 2026

This study addresses the performance bottlenecks in long-horizon whole-body mobile manipulation for humanoid robots caused by reward bias and catastrophic forgetting. To this end, we propose a unified policy framework that introduces parallel streaming training to eliminate sequential dependencies, dynamic initialization to enhance phase-boundary robustness, and a reward gating mechanism to prevent interference with previously placed objects. Furthermore, multi-scenario reinforcement learning is synergistically optimized via a shared policy network. Experimental results on the LHM-Humanoid benchmark demonstrate that our approach achieves single-phase success rates exceeding 80% and significantly outperforms baselines on long-sequence tasks with graceful performance degradation. Overall, the proposed method effectively improves generalization and stability in complex manipulation scenarios.

0 citationsRead paper