让机器人通过共享基础动作模块，持续学习新技能而不遗忘旧技能。
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
- 用可复用的原始提示词捕捉不同技能间的共性动作
- 在新任务中追加提示词，实现旧知识向新任务迁移
- 适合长期学习新操作技能的机器人系统
构建能持续学习并有效利用已有知识的长期机器人仍面临巨大挑战。尽管经验回放和参数高效方法在缓解灾难性遗忘方面取得成功，但简单应用这些方法无法充分利用不同技能间的共享基础动作。为此，我们提出原始提示学习（PPL），通过可重用、可扩展的基础提示实现长期机器人操作。该方法分两阶段：首先在多技能预训练阶段学习一组原始提示，捕捉跨技能的语义与运动共性；其次在长期学习过程中，对新技能追加提示词并优化，同时冻结已训练提示，实现旧知识向新任务的知识迁移。我们在仿真与真实世界任务中构建大规模技能数据集并进行广泛实验，结果表明PPL优于当前最先进方法。
原文摘要 · Abstract (English)
Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alleviating catastrophic forgetting problem, naively applying these methods causes a failure to leverage the shared primitives between skills. To tackle these issues, we propose Primitive Prompt Learning (PPL), to achieve lifelong robot manipulation via reusable and extensible primitives. Within our two stage learning scheme, we first learn a set of primitive prompts to represent shared primitives through multi-skills pre-training stage, where motion-aware prompts are learned to capture semantic and motion shared primitives across different skills. Secondly, when acquiring new skills in lifelong span, new prompts are appended and optimized with frozen pretrained prompts, boosting the learning via knowledge transfer from old skills to new ones. For evaluation, we construct a large-scale skill dataset and conduct extensive experiments in both simulation and real-world tasks, demonstrating PPL's superior performance over state-of-the-art methods.
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