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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=659742
• 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的，混乱的世界中工作，导致应对建筑，学习和评估等关键挑战。移动机器人正在从刚性，二维导航平台到合规和交互式机器，这些机器可以完成更复杂和复杂的任务，假设他们可以聪明地利用其高级可助力。 ******为此，我的发现研究计划的长期目标是如何整合所需的功能和决策过程，以便机器人可以长期在现实世界中运行。 ******因此，我开发了IRL-1，这是一种符合自然互动互动（姿势，导航，听觉，视觉，语言，语言，触摸，触摸，手势，情感）的符合性的全向类人形移动机器人。该平台提供了一个非凡的测试床，以解决高级机器人控制体系结构必须面对的集成问题。我的提案的重点是研究如何从机器人的外部和内部观点来记忆，分类和学习的数据，以提高对其互动的理解，以动态与其操作环境，以实现智能和自主决策。我计划整合能够允许IRL-1知道与人互动时与谁，谁或发生什么的功能，以确定下一步该怎么做，使用可以适应环境中经历的情况的内存模型其功能有限。内存管理方法将确定要保留在机器人的工作内存中的哪些信息，以便满足在线处理约束。类似情节的记忆模型将学会分类机器人的经验，预测和影响其意图，以提高性能和互动。空间和类似情节的记忆模型都将相互补充，以验证和推断高级概念。考虑到事件的历史，随着时间的推移行为开发和意图的历史也将使IRL-1能够根据感知事件以及使用其自身的控制和推理过程来自我表征其经验。 ****** IRL-1将被编程为完成任务，目的是迭代，时空关于空间和事件的时空知识，并从过去的经验中获得知识。绩效将以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动运行的整体能力来衡量。从多种方式中实验和整合学习能力将为拥有服务或辅助机器人在自然环境中自动运行的基础提供基础，并提高我们对我们自己的智能的理解。**********