Learning, Memorization and Cognition in an Autonomous Robot Control Architecture

自主机器人控制架构中的学习、记忆和认知

• 批准号: RGPIN-2016-05096
• 负责人: Michaud, François
• 金额: $ 2.99万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=709588
• 关键词: Learning; Memorization; Cognition; Autonomous; Robot

The ultimate goal of Artificial Intelligence is to work in the wild, messy world, leading to address key challenges such as architecture, learning and evaluation. Mobile robots are evolving from being rigid, two-dimensional navigation platforms, to compliant and interactive machines that can accomplish more complex and sophisticated tasks, assuming that they can exploit their advanced capabilities intelligently. To do so, the long term objective of my Discovery research program is the study of how to integrate the required capabilities and decision-making processes so that robots can operate in the real world over long periods of time. Consequently, I developed IRL-1, a compliant omnidirectional humanoid mobile robot capable of natural reciprocal interaction (pose, navigation, auditory, visual, language, touch, gesture, affect) with humans. This platform provides an exceptional test bed to address the integration issues that a high-level robot control architecture has to face. The focus of my proposal is to study how data from the robot's external and internal perspectives can be memorized, categorized and learned, to improve the understanding of its interaction dynamic with its operating environment for intelligent and autonomous decision-making. I plan to integrate capabilities that will allow IRL-1 to know where, with whom or what happened when interacting in the world with people, to have it determine what to do next using memory models that can adapt to the situations experienced in the environment and its limited capabilities. A memory management approach will determine which information to keep in the robot's working memory so that online processing constraints are satisfied. An episodic-like memory model will learn to categorize the experiences of the robot, to predict and to influence its intentions for more efficient performance and interaction. Both spatial and episodic-like memory models will complement each other to validate and infer high-level concepts. Taking into consideration the history of events, behavior exploitation and intentions over time will also provide IRL-1 with the ability to self-characterize its experiences based on perceived events and the use of its own control and reasoning processes. IRL-1 will be programmed to accomplish tasks with the intent to acquire, iteratively, spatio-temporal knowledge about space and events, and gain knowledge over time from past experiences. Performance will be measured in terms of the added capabilities brought by the high-level control mechanisms, and from the robot's overall ability to operate autonomously in diverse conditions. Experimenting and integrating learning capabilities from multiple modalities will provide essential insights at the foundation of what is required for having service or assistive robots operate autonomously in natural settings, and improve our understanding of our own intelligence.

人工智能的最终目标是在狂野、混乱的世界中工作，从而解决架构、学习和评估等关键挑战。移动机器人正在从刚性的二维导航平台发展为兼容的交互式机器，可以完成更复杂和复杂的任务，前提是它们可以智能地利用其先进的功能。 为此，我的发现研究计划的长期目标是研究如何整合所需的能力和决策过程，以便机器人能够在现实世界中长时间运行。 因此，我开发了 IRL-1，这是一种兼容的全向人形移动机器人，能够与人类进行自然交互（姿势、导航、听觉、视觉、语言、触摸、手势、情感）。该平台提供了一个特殊的测试平台，可以解决高级机器人控制架构必须面对的集成问题。我的建议的重点是研究如何记忆、分类和学习机器人外部和内部视角的数据，以提高对其与其操作环境的交互动态的理解，从而实现智能和自主决策。我计划集成一些功能，让 IRL-1 知道在世界上与人们互动时在哪里、与谁或发生了什么，让它使用能够适应环境中经历的情况的记忆模型来确定下一步该做什么其能力有限。内存管理方法将确定将哪些信息保留在机器人的工作内存中，以便满足在线处理约束。类似情景的记忆模型将学习对机器人的体验进行分类，预测并影响其意图，以实现更有效的性能和交互。空间记忆模型和情景记忆模型将相互补充，以验证和推断高级概念。考虑到事件的历史、行为利用和随着时间的推移的意图，IRL-1也将能够根据感知到的事件以及使用自己的控制和推理过程来自我表征其经历。 IRL-1 将被编程来完成任务，旨在迭代地获取有关空间和事件的时空知识，并随着时间的推移从过去的经验中获取知识。性能将根据高级控制机制带来的附加功能以及机器人在不同条件下自主操作的整体能力来衡量。实验和整合多种方式的学习能力将为服务或辅助机器人在自然环境中自主操作所需的基础提供重要的见解，并提高我们对自身智能的理解。