解决家务机器人与人互动时的冲突问题，让机器人更懂用户偏好。
COMMET: A System for Human-Induced Conflicts in Mobile Manipulation of Everyday Tasks
- 多模态检索+微调模型混合检测冲突，提升准确率与响应速度。
- 利用GPT-4o分析用户历史选择，自动学习个性化偏好。
- 提供易用界面收集数据，适合家庭服务机器人研发者使用。
机器人与AI技术的进步正推动机器人从工业场景进入日常生活。然而，日常环境中动态且不可预测的人类行为常与机器人操作产生直接或间接冲突。由于这类人因冲突具有社会属性，解决方案往往不唯一，高度依赖用户个人偏好。为应对挑战并推动家用机器人发展，我们提出COMMET系统，用于处理日常任务中移动操作时的人因冲突。COMMET采用混合检测方法：先通过多模态检索初步识别，对置信度低的情况再启用微调模型进行精细化推理。基于收集的用户偏好选项与设置，GPT-4o将总结相关案例中的用户偏好。初步实验表明，该检测模块在准确率和延迟方面优于纯GPT模型。为促进后续研究，我们还设计了友好的用户数据采集界面，并展示了实际部署的有效工作流程。
原文摘要 · Abstract (English)
Continuous advancements in robotics and AI are driving the integration of robots from industry into everyday environments. However, dynamic and unpredictable human activities in daily lives would directly or indirectly conflict with robot actions. Besides, due to the social attributes of such human-induced conflicts, solutions are not always unique and depend highly on the user's personal preferences. To address these challenges and facilitate the development of household robots, we propose COMMET, a system for human-induced COnflicts in Mobile Manipulation of Everyday Tasks. COMMET employs a hybrid detection approach, which begins with multi-modal retrieval and escalates to fine-tuned model inference for low-confidence cases. Based on collected user preferred options and settings, GPT-4o will be used to summarize user preferences from relevant cases. In preliminary studies, our detection module shows better accuracy and latency compared with GPT models. To facilitate future research, we also design a user-friendly interface for user data collection and demonstrate an effective workflow for real-world deployments.
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