点击下方 卡片 ，关注" 具身智能 之心 "公众号 编辑丨具身智能之心 本文只做学术分享，如有侵权，联系删文 研究指出，灾难性遗忘的根源在于 VLM 的互联网级预训练数据与机器人微调数据之间存在 分布不匹配 —— 预训练数据以图文对为主，而机器人数据 以低维动作向量为主，这种差异迫使研究者采用全参数微调，进一步加剧了对预训练知识的覆盖。 论文：Actions as Language: Fine-Tuning VLMs into VLAs Without Catastrophic Forgetting 链接：https://arxiv.org/pdf/2509.22195 项目：https://vlm2vla.github.io/ VLM2VLA 在做什么？ VLM2VLA 的核心思路是 从数据层面解决分布不匹配问题 ，将低维动作转化为自然语言描述，使 VLA 微调数据与 VLM 预训练的图文分布对齐，进 而仅通过低秩适应（LoRA）微调即可实现动作生成，最小化对 VLM backbone 的修改，最终避免灾难性遗忘。 VLM2VLA训练范式首先通过自然语言表征底层动作，在数据层面解决分布失配问题。这种对齐机制 ...

近日，北京大学王选计算机研究所周嘉欢助理教授与彭宇新教授合作在人工智能重要国际期刊 IEEE TPAMI 发布一项最新的研究成果： DKP++（Distribution- aware Knowledge Aligning and Prototyping for Non-exemplar Lifelong Person Re-Identification） 。该工作针对终身学习中的灾难性遗忘问题，提出分布建模引导 的知识对齐与原型建模框架，不仅有效增强了对历史知识的记忆能力，也提升了模型的跨域学习能力。 本文的第一作者为北京大学北京大学王选计算机研究所助理教授周嘉欢，通讯作者为北京大学王选计算机研究所教授彭宇新。目前该研究已被 IEEE TPAMI 接 收，相关代码已开源。 行人重识别（Person Re-Identification, ReID）旨在针对跨相机视角、跨地点、跨时间等场景中，基于视觉特征实现对同一行人图像的匹配与关联。该技术在多摄像 头监控、智能交通系统、城市安全管理以及大规模图像视频检索等实际场景中具有广泛应用价值。然而，在现实环境中，由于采集地点、拍摄设备和时间条件的 不断变化，行人图像的分 ...

Group 1 - The year 2024 is referred to as the "Year of Humanoid Robots," with predictions that emotional communication between humans and robots will become a norm in future intelligent societies [1] - The concept of machine emotions and AI companionship raises questions about the impact on human-machine interaction and relationships, as well as cultural and gender perspectives on these emotional connections [1] - The discussions highlight the potential social impacts, technological risks, and ethical issues arising from human-robot emotional interactions, prompting interdisciplinary research [1] Group 2 - The concept of machine emotions is defined and analyzed through emotional intelligence, human-machine emotions, and human-machine interaction, advocating for a limited approach to the development of machine emotions [2] - A new perspective on endowing machines with emotional capabilities is proposed based on a life-centered consciousness theory, suggesting that simulating biological homeostasis can lead to autonomous adaptability in machines [2] - Ethical reflections on human-machine emotional interactions, particularly in the context of AI resurrection technology, reveal risks such as emotional dependency and identity crises, necessitating regulatory and cultural adjustments [2] Group 3 - The philosophical discussions in affective computing often rely on idealized technical assumptions, overlooking the importance of forgetting mechanisms in creating realistic and ethical AI emotional systems [3][4] - The current challenges in affective computing include the reliance on data quality and the superficiality of emotional expressions in AI systems, which fail to capture the complexity of human emotional experiences [6] - The introduction of forgetting mechanisms is essential for enhancing the adaptability and authenticity of emotional AI, allowing systems to discard outdated emotional data [11][12] Group 4 - The proposed phenomenology-inspired human-like forgetting neural model (PHFNM) aims to integrate individual and collective forgetting processes in emotional AI systems, reflecting both natural decay and active forgetting [19][22] - The model consists of three interconnected layers: a low-dimensional emotional index layer for natural decay, a memory encoding layer for dynamic reconstruction, and an active forgetting layer for ethical regulation [23][24][25] - The PHFNM framework emphasizes the need for a balance between individual emotional memory and collective social interactions, ensuring that emotional AI systems remain relevant and ethically responsible [26][27]