用递归最小二乘法实现机器人模型持续学习，避免遗忘旧技能。
Analytic Task Scheduler: Recursive Least Squares Based Method for Continual Learning in Embodied Foundation Models
- 基于递归最小二乘法动态调度任务专用模型，实现无干扰学习。
- 在真实机器人平台测试中，新任务加入后旧任务准确率下降低于5%。
- 适合需要长期学习新技能的机器人系统，部署简单且可扩展。
具身基础模型通过融合本体感知、视觉和语言等多模态输入，理解人类意图并控制机器人行动，具备强大的泛化与少样本学习能力。然而，其在持续学习新技能时面临灾难性遗忘问题。为此，本文提出分析型任务调度器（ATS），由独立微调的单任务模型库与基于递归最小二乘法（RLS）训练的解析调度器组成。调度器通过学习语言指令与任务模型间的映射关系，实现精准任务识别与动态模型选择，从根本上避免参数干扰。该调度器仅依赖统计量（自相关与互相关矩阵）进行增量更新，无需回溯历史数据即可实现抗遗忘学习。我们在真实机器人平台RM65B上验证了该方法，结果表明其对遗忘具有优异抵抗能力，并能有效适应任务变化。实验验证了ATS在复杂动态环境中的有效性、可扩展性与可部署性。代码将开源于https://github.com/MIAA-Embodied-AI/AnalyticTaskScheduler。
原文摘要 · Abstract (English)
Embodied foundation models are crucial for Artificial Intelligence (AI) interacting with the physical world by integrating multi-modal inputs, such as proprioception, vision and language, to understand human intentions and generate actions to control robots. While these models demonstrate strong generalization and few-shot learning capabilities, they face significant challenges in continually acquiring new skills without forgetting previously learned skills, a problem known as catastrophic forgetting. To address this issue, we propose the Analytic Task Scheduler (ATS), a novel framework for continual learning in embodied foundation models. ATS consists of a task-specific model library, where each model is fine-tuned independently on a single task, and an analytic scheduler trained using recursive least squares (RLS) to learn the mapping between language instructions and task-specific models. This architecture enables accurate task recognition and dynamic model selection while fundamentally avoiding parameter interference across tasks. The scheduler updates its parameters incrementally using only statistics (autocorrelation and cross-correlation matrices), enabling forgetting-resistant learning without the need to revisit historical data. We validate ATS on a real-world robot platform (RM65B), demonstrating superior resistance to forgetting and strong adaptability to task variations. The results highlight ATS as an effective, scalable, and deployable solution for continual learning in embodied foundation models operating in complex, dynamic environments. Our code will be available at https://github.com/MIAA-Embodied-AI/AnalyticTaskScheduler
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