让大模型持续学习新场景下的共情动作生成，提升真实世界泛化能力。
Towards Closed-Loop Embodied Empathy Evolution: Probing LLM-Centric Lifelong Empathic Motion Generation in Unseen Scenarios
- 用专家混合架构分离情绪与场景特征，实现跨场景共情动作生成。
- 在多个新场景数据集上超越现有方法，性能显著提升。
- 适合关注具身智能、持续学习与情感计算的研究者。
现有以人类为中心的情绪动作生成方法多聚焦于单一固定数据集上的性能提升，忽视了体育、舞蹈等动态扩展场景下的灵活生成能力，而有效学习这些新兴场景可显著增强模型的真实世界泛化性。受此启发，本文提出一种新型的以大语言模型为核心的终身共情动作生成任务（L^2-EMG），旨在使大语言模型具备在不同未见场景中持续获取情绪动作生成知识的能力，助力构建具备共情与智能的闭环自进化具身智能体。进一步，本文识别出该任务中的两个关键挑战：情绪解耦难题与场景适应难题。为此，提出一种情绪可迁移且场景自适应的专家混合（ES-MoE）方法，分别设计因果引导的情绪解耦模块与场景自适应专家构建模块以应对上述挑战。尤其地，本文构建了多个用于验证的L^2-EMG数据集。大量实验表明，所提方法在多个未见场景下均显著优于先进基线模型。
原文摘要 · Abstract (English)
In the literature, existing human-centric emotional motion generation methods primarily focus on boosting performance within a single scale-fixed dataset, largely neglecting the flexible and scale-increasing motion scenarios (e.g., sports, dance), whereas effectively learning these newly emerging scenarios can significantly enhance the model's real-world generalization ability. Inspired by this, this paper proposes a new LLM-Centric Lifelong Empathic Motion Generation (L^2-EMG) task, which aims to equip LLMs with the capability to continually acquire emotional motion generation knowledge across different unseen scenarios, potentially contributing to building a closed-loop and self-evolving embodied agent equipped with both empathy and intelligence. Further, this paper poses two key challenges in the L^2-EMG task, i.e., the emotion decoupling challenge and the scenario adapting challenge. To this end, this paper proposes an Emotion-Transferable and Scenario-Adapted Mixture of Experts (ES-MoE) approach which designs a causal-guided emotion decoupling block and a scenario-adapted expert constructing block to address the two challenges, respectively. Especially, this paper constructs multiple L^2-EMG datasets to validate the effectiveness of the ES-MoE approach. Extensive evaluations show that ES-MoE outperforms advanced baselines.
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