Shared autonomy holds promise for improving the usability and accessibility of assistive robotic arms, but current methods often rely on costly expert demonstrations and remain static after pretraining, limiting their ability to handle real-world variations. Even with extensive training data, unforeseen challenges--especially those that fundamentally alter task dynamics, such as unexpected obstacles or spatial constraints--can cause assistive policies to break down, leading to ineffective or unreliable assistance. To address this, we propose ILSA, an Incrementally Learned Shared Autonomy framework that continuously refines its assistive policy through user interactions, adapting to real-world challenges beyond the scope of pre-collected data. At the core of ILSA is a structured fine-tuning mechanism that enables continual improvement with each interaction by effectively integrating limited new interaction data while preserving prior knowledge, ensuring a balance between adaptation and generalization. A user study with 20 participants demonstrates ILSA's effectiveness, showing faster task completion and improved user experience compared to static alternatives. Code and videos are available at https://ilsa-robo.github.io/.

翻译：共享自主性有望提升辅助机械臂的可用性和可及性，但现有方法通常依赖于昂贵的专家演示，且在预训练后保持静态，限制了其处理现实世界变化的能力。即使拥有大量训练数据，未预见的挑战——尤其是那些根本改变任务动态的挑战，如意外障碍或空间约束——可能导致辅助策略失效，从而产生无效或不可靠的辅助。为解决这一问题，我们提出了ILSA，一种增量学习的共享自主性框架，该框架通过用户交互持续优化其辅助策略，以适应超出预收集数据范围的现实世界挑战。ILSA的核心是一种结构化微调机制，通过有效整合有限的新交互数据并保留先验知识，实现每次交互后的持续改进，确保适应性与泛化性之间的平衡。一项包含20名参与者的用户研究证明了ILSA的有效性，相较于静态替代方案，其任务完成速度更快，用户体验更佳。代码与视频可在 https://ilsa-robo.github.io/ 获取。