具達成內部平衡之決策模型的使用者感知自主服務型機器人

A Homeostasis Based Decision Making System on Human-Aware Autonomous Service Robot

林敬(Ching Lin)

指導教授 ： 傅立成

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2016年)

https://doi.org/10.6342/NTU201603154

摘要

服務型機器人需要能夠選擇行為,在缺乏使用者命令下自行進行決策,甚至主動提供服務,才能被稱為「自主」。對於掃地機器人、取物機器人等單用途機器人而言,由於它們有個明確的目標,因此可以人工建構一個完整的決策模型作為它們的行為準則。但是對於多用途機器人而言,他們的目標較為曖昧、模糊,甚至沒有明確目標,此時便較難以為他們建立完整的行為模型,使得它們的自主性較低。
「內部平衡理論 (homeostatic drive theory)」是一個在社交機器人中常見的決策理論,它使機器人試著維持其內部狀態的恆定,並根據自身的需求選擇行為。雖然此方法可以提高機器人的自主性,由於此方法忽略了使用者的需求,讓「使用者感知」的能力降低,因此需要調整才能應用於服務型機器人身上。本篇論文將「使用者意圖」以及「使用者回饋」結合至內部平衡理論中,讓決策模型更以使用者為中心,同時保有機器人的高自主性。機器人的內部需求 (drives) 將轉化為動機(motivations),且機器人將同時考慮自身的動機以及使用者的意圖來決定自身的行為。機器人每個行為的效果並非事先定義好的,而是在互動中利用增強式學習 (reinforcement learning) 所得,使得機器人對於環境以及使用者的先前知識的需求都能降到最低。此決策模型於模擬環境中進行測試及訓練,並將機器人在模擬環境中所學知識轉移至真實的機器人進行實地測試。結果顯示機器人在滿足使用者需求的同時也能夠維持自己體內的恆定,提升自主運作時間,同時達成高自主性以及使用者感知能力。

關鍵字

人工智慧 ； 機器人 ； 機器學習

並列摘要

For a service robot to reach high autonomy, it should choose what to do, make it’s own decisions without user command, and even provide service to the user proactively. For single purpose robots, such as object fetching robots or cleaning robots, since a specific goal is given to each of them, the well-structured decision processes could easily proceed, and decision about that task could be made. However, for robots with vague goals or no specific goal at all, such as caring robots or personal service robots, it is harder to construct a general purpose decision process for them, lowering their autonomy. Homeostasis drive theory is a dominating psychological approach in decision making for social robots. A robot adopting this theory would try to maintain its internal status, and act according to its own need. While achieve better autonomy, this approach ignores the needs of its human user, resulting in low degree of human awareness. This work integrated human intention and human feedback into a homeostasis based system, making the decision process more user-centric, while maintaining high autonomy. The robot’s internal needs (drives) generate motivations, and the robot will choose its actions considering both the need of the user and its own motivation. The effects of its actions are not predefined and are learned during interactions by reinforcement learning, making the system require little prior knowledge about the user. The proposed system has been tested in simulations and on a real robot. The results show that the robot can not only satisfy its own needs but also serve the user proactively.

並列關鍵字

artificial intelligence ； robot ； machine learning

參考文獻

[1] E. Garcia, M. A. Jimenez, P. G. De Santos, and M. Armada, “The evolution of robotics research,” IEEE Robotics & Automation Magazine, vol. 14, no. 1, pp. 90–103, 2007.

[2] “irobot: Your partner for a cleaner home,” 2016, accessed: 10-July-2016. [Online]. Available: www.irobot.com

[3] C. Jayawardena, I. H. Kuo, U. Unger, A. Igic, R. Wong, C. I. Watson, R. Stafford, E. Broadbent, P. Tiwari, J. Warren et al., “Deployment of a service robot to help older people,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010, pp. 5990–5995.

[4] C. Jayawardena, I. Kuo, C. Datta, R. Stafford, E. Broadbent, and B. MacDonald, “Design, implementation and field tests of a socially assistive robot for the elderly: Healthbot version 2,” in Proceedings of IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), 2012, pp. 1837–1842.

[5] P. Elinas and J. J. Little, “Decision theoretic task coordination for a visually-guided interactive mobile robot,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2007, pp. 4108–4114.