SPECI：基于技能提示的机器人操作分层持续模仿学习
SPECI: Skill Prompts based Hierarchical Continual Imitation Learning for Robot Manipulation
AI总结：
针对现有持续模仿学习忽视技能特性、知识迁移欠佳的问题，提出SPECI分层架构，通过技能码本与注意力机制实现技能复用，并引入模式近似增强任务迁移，实验全面超越现有方法。
摘要：
在动态非结构化环境中的真实世界机器人操作需要对新出现的物体、场景和任务具备终身适应性。传统的模仿学习依赖于静态训练范式，不适合终身适应。尽管持续模仿学习（CIL）能够在保留已学知识的同时实现增量任务适应，但当前的CIL方法主要忽视了机器人操作的内在技能特征，或依赖于手动定义且僵化的技能，导致跨任务知识迁移效果欠佳。为解决这些问题，我们提出了基于技能提示的分层持续模仿学习（SPECI），一种用于机器人操作的新型端到端分层CIL策略架构。SPECI框架由多模态感知与融合模块（用于异构感官信息编码）、高层技能推理模块（用于动态技能提取与选择）以及低层动作执行模块（用于精确动作生成）组成。为了在技能和任务两个层面实现高效的知识迁移，SPECI通过可扩展的技能码本和注意力驱动的技能选择机制，进行持续的隐式技能获取与复用。此外，我们引入模式近似来增强后两个模块，使其具备任务特定和任务共享参数，从而加强任务层面的知识迁移。在多种操作任务套件上的广泛实验表明，SPECI在所有评估指标上持续优于最先进的CIL方法，展现出卓越的双向知识迁移能力和优越的整体性能。
英文摘要：
Real-world robot manipulation in dynamic unstructured environments requires lifelong adaptability to evolving objects, scenes and tasks. Traditional imitation learning relies on static training paradigms, which are ill-suited for lifelong adaptation. Although Continual Imitation Learnin (CIL) enables incremental task adaptation while preserving learned knowledge, current CIL methods primarily overlook the intrinsic skill characteristics of robot manipulation or depend on manually defined and rigid skills, leading to suboptimal cross-task knowledge transfer. To address these issues, we propose Skill Prompts-based HiErarchical Continual Imitation Learning (SPECI), a novel end-to-end hierarchical CIL policy architecture for robot manipulation. The SPECI framework consists of a multimodal perception and fusion module for heterogeneous sensory information encoding, a high-level skill inference module for dynamic skill extraction and selection, and a low-level action execution module for precise action generation. To enable efficient knowledge transfer on both skill and task levels, SPECI performs continual implicit skill acquisition and reuse via an expandable skill codebook and an attention-driven skill selection mechanism. Furthermore, we introduce mode approximation to augment the last two modules with task-specific and task-sharing parameters, thereby enhancing task-level knowledge transfer. Extensive experiments on diverse manipulation task suites demonstrate that SPECI consistently outperforms state-of-the-art CIL methods across all evaluated metrics, revealing exceptional bidirectional knowledge transfer and superior overall performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
