Skill Incremental Learning (SIL) is the process by which an embodied agent expands and refines its skill set over time by leveraging experience gained through interaction with its environment or by the integration of additional data. SIL facilitates efficient acquisition of hierarchical policies grounded in reusable skills for downstream tasks. However, as the skill repertoire evolves, it can disrupt compatibility with existing skill-based policies, limiting their reusability and generalization. In this work, we propose SIL-C, a novel framework that ensures skill-policy compatibility, allowing improvements in incrementally learned skills to enhance the performance of downstream policies without requiring policy re-training or structural adaptation. SIL-C employs a bilateral lazy learning-based mapping technique to dynamically align the subtask space referenced by policies with the skill space decoded into agent behaviors. This enables each subtask, derived from the policy's decomposition of a complex task, to be executed by selecting an appropriate skill based on trajectory distribution similarity. We evaluate SIL-C across diverse SIL scenarios and demonstrate that it maintains compatibility between evolving skills and downstream policies while ensuring efficiency throughout the learning process.

翻译：技能增量学习（SIL）是指具身智能体通过与环境的交互或整合额外数据，利用所获经验随时间扩展并精炼其技能集的过程。SIL促进了基于可复用技能、面向下游任务的分层策略的高效习得。然而，随着技能库的演进，其可能破坏与现有基于技能的策略的兼容性，从而限制策略的可复用性与泛化能力。本文提出SIL-C，一种确保技能-策略兼容性的新型框架，使得增量学习技能的改进能够提升下游策略的性能，而无需策略重新训练或结构适配。SIL-C采用一种基于双边惰性学习的映射技术，动态地对齐策略所引用的子任务空间与解码为智能体行为的技能空间。这使得从策略对复杂任务分解得到的每个子任务，能够基于轨迹分布相似性选择合适的技能来执行。我们在多种SIL场景下评估SIL-C，结果表明该框架在保持学习过程高效性的同时，确保了演进技能与下游策略之间的兼容性。