针对人形机器人搬运负载变化的鲁棒控制难题，提出分治适配新方法。
SplitAdapter: Load-Aware Humanoid Loco-Manipulation via Factorized Adaptation
- 将负载与动力学因素分离建模，通过双编码器增强适应性
- 在2~6公斤负载、0~60厘米高度下成功率显著提升，重载时优势更明显
- 适合需要高鲁棒性的真实场景人形机器人任务部署
人形机器人在搬运过程中需在不同物体质量及抓取/放置高度下保持全身稳定控制。这一问题在仿真到现实迁移中尤为严峻，因物体负载变化与机器人动力学不匹配在物理接触时相互作用。现有基于历史的适配器常将这些因素压缩为单一潜在表示，削弱了重载操作下的鲁棒性。本文提出SplitAdapter：通过冻结预训练的箱体搬运策略，引入负载与动力学感知的上下文编码器，结合分域世界模型目标、基于梯度反转的交叉对抗正则化以及分层特征线性调制（FiLM）进行训练。在模拟到模拟实验和真实世界部署中，相较于基线策略与世界模型FiLM方法，SplitAdapter在2、4、6公斤负载及0、30、60厘米高度条件下均实现全任务成功率提升，尤其在重载情况下表现最优。
原文摘要 · Abstract (English)
Humanoid loco-manipulation requires stable whole-body control under varying object masses and pickup/placement heights. This becomes particularly challenging in sim-to-real transfer, where object-induced load variation and robot-side dynamics mismatch interact during physical contact. Existing history-based adapters often compress these factors into a single latent representation, which can weaken robustness under heavy-load manipulation. We propose \textbf{SplitAdapter: Load-Aware Humanoid Loco-Manipulation via Factorized Adaptation}, which freezes a pretrained box manipulation policy and extends it with object/load and dynamics-aware context encoders trained with split world-model objectives, GRL-based cross-adversarial regularization, and hierarchical Feature-wise Linear Modulation (FiLM). In sim-to-sim experiments and real-world deployment, SplitAdapter improves Full-task success over the base policy and world-model FiLM baselines across object masses of $2$, $4$, and $6$ kg and pickup/placement heights of $0$, $30$, and $60$ cm, with the largest improvements under heavy-load conditions.
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