发表机构
Utrecht University; University of Cambridge（乌得勒支大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对社交机器人在多样上下文场景下的持续学习问题，提出EDD框架解耦环境与社会知识，缓解遗忘，性能优于现有基线，相关代码已公开。
AI 中文摘要
社交机器人需在多样环境中运行，相似场景可能对应不同的社会适配动作，例如在拥挤的家庭中开启对话是可接受的，但在办公室会议中则会造成干扰。由于无法预先预判所有此类规范与环境，机器人需要持续学习（CL），以从序列经验中自适应调整，同时保留已习得的知识。过往研究已探讨了社交适配机器人动作生成的持续学习，但未解决领域增量设置问题——在此设置中，机器人会逐步遇到多样上下文（如客厅、会议室、办公室、走廊），其中环境线索（如空间是否开阔或是否堆满家具）与社会线索（如人或其他智能体在机器人周围的位置）共同决定机器人动作的适配性。我们通过显式解耦双分支（EDD）框架填补这一空白。EDD明确分离与环境、社会智能体相关的知识，并使用基于回放的 rehearsal 缓解遗忘，同时在多个室内领域中学习机器人动作（如清洁、服务、开启对话）的适配性。实验表明，EDD的性能优于多个当前最优基线，消融研究进一步评估了不同解耦策略及对领域排序的敏感性。我们的代码可在该 https URL 公开获取。
英文摘要
Social robots are expected to operate across diverse environments, where similar arrangements can imply different socially appropriate actions, e.g., starting a conversation may be acceptable in a crowded home but disruptive in an office meeting. Because such norms and environments cannot all be anticipated in advance, robots require continual learning (CL) to adapt from sequential experience while retaining previously acquired knowledge. Prior work has studied CL for generating socially appropriate robot actions, but it has not addressed domain-incremental settings in which the robot incrementally encounters diverse contexts (e.g., living room, meeting room, office, hallway), where both environmental (e.g., whether the space is open or cluttered with furniture) and social cues (e.g., how people or other agents are positioned around the robot) jointly shape the appropriateness of robot actions. We address this gap with the Explicit Disentanglement Dual-Branch (EDD) framework. EDD explicitly separates environmental and social-agent related knowledge and uses replay-based rehearsal to mitigate forgetting while learning the appropriateness of robot actions (e.g., cleaning, serving, starting a conversation) across several indoor domains. Experiments show that EDD outperforms several state-of-the-art baselines, and ablation studies further evaluate different disentanglement strategies and the sensitivity to domain ordering. Our code is publicly available at https://github.com/Cambridge-AFAR/Mind-the-Context.git.
CommentsExtended version of the paper accepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
