让服务机器人像人一样自觉思考，应对未知场景。
Towards Conscious Service Robots
- 借鉴人类双系统认知，设计可自适应的机器人架构
- 结合因果模型与元认知，提升对新环境的适应能力
- 适合研究智能机器人自主决策与自我监控的学者
深度学习在感知、自然语言处理等领域的成功激发了对自主机器人进步的期待。然而，现实世界中的机器人面临变化性、高维状态空间、非线性依赖和部分可观测性等挑战。关键问题是机器人、环境与任务的非平稳性，导致分布外数据时性能下降。与当前机器学习模型不同，人类因具备支持系统化泛化和元认知的认知架构，能快速适应变化与新任务。人类大脑的系统1处理常规任务无意识进行，系统2负责复杂任务的有意识管理，促进灵活解决问题与自我监控。为使机器人实现类人学习与推理，需整合因果模型、工作记忆、规划与元认知处理。通过融入人类认知洞见，下一代服务机器人将能够应对新情境，并自我监控以避免风险与减少错误。
原文摘要 · Abstract (English)
Deep learning's success in perception, natural language processing, etc. inspires hopes for advancements in autonomous robotics. However, real-world robotics face challenges like variability, high-dimensional state spaces, non-linear dependencies, and partial observability. A key issue is non-stationarity of robots, environments, and tasks, leading to performance drops with out-of-distribution data. Unlike current machine learning models, humans adapt quickly to changes and new tasks due to a cognitive architecture that enables systematic generalization and meta-cognition. Human brain's System 1 handles routine tasks unconsciously, while System 2 manages complex tasks consciously, facilitating flexible problem-solving and self-monitoring. For robots to achieve human-like learning and reasoning, they need to integrate causal models, working memory, planning, and metacognitive processing. By incorporating human cognition insights, the next generation of service robots will handle novel situations and monitor themselves to avoid risks and mitigate errors.
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