Interactive autonomous machines

交互式自主机器

• 批准号: RGPIN-2022-04556
• 负责人: Jenkin, Michael
• 金额: $ 1.75万
• 依托单位: York University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=752427
• 关键词: Interactive; autonomous; machines

Although there have been significant strides in our ability to design and deploy autonomous systems, key problems remain unsolved that are critical to future advances in this field. My research program concentrates on three of these key problems: multi-cue integration, plan development and reasoning, and human-robot interaction. Multi-cue integration: An autonomous machine must be able to capture sensory data from a range of different technologies to build a model of where it is and how it can move within its environment. The underlying approach here is to understand how humans build similar representations and to leverage this knowledge to solve the problem for machines. Over the last ten years or so I have been working with a group of international collaborators to understand how humans develop a sense of self-orientation and self-motion. Key here is performing controlled experiments in environments within which humans are presented with unusual cue combinations to illuminate the underlying cue integration process. With the completion of recent work in long duration bed rest, I have helped develop a deep and wide dataset of human self-orientation perception which I plan to use to construct a time-dependent cue integration model that will be evaluated using underwater robots. Plan development and execution: The machine must be able to reason about its environment and how it can act upon that environment. Traditionally, long term robot plans were developed using mechanisms that decomposed tasks based on logical structures. Although effective in its day, these planning approaches have been eclipsed by modern AI approaches (e.g, Deep Reinforcement Learning - DRL) that have demonstrated extraordinary capabilities, especially for tasks with reasonably short-term horizons for which appropriate training can be performed. How can we best integrate the lessons learned from classic planning regimes with the capabilities of mechanisms like DRL? Utilizing a set of specific tasks - invasive water plant monitoring and indoor environment monitoring - I plan to explore how best to use DRL to learn short-term plans that can be sequenced to provide task solutions for these complex, but highly structured tasks. Human-robot interaction (HRI): The machine must be able to interact with the environment and most critically it must be able to interact with humans that occupy the environment within which it is operating. My preliminary work suggests task-specific gesture language can be effective for HRI, but can we better leverage the underlying task structure to build effective HRI systems? Utilizing common commercial diver tasks, I plan to generalize my preliminary work here to condition language token and conversational structure priors within the HRI representation, and to leverage both in understanding human to robot communication.

尽管我们在设计和部署自主系统的能力方面取得了重大进展，但对该领域未来发展至关重要的关键问题仍未解决。我的研究计划集中在这些关键问题中的三个：多线索集成，计划开发和推理，以及人机交互。多线索整合：自主机器必须能够从一系列不同的技术中捕获传感数据，以建立一个模型，了解它在哪里以及如何在环境中移动。这里的基本方法是了解人类如何构建类似的表示，并利用这些知识为机器解决问题。在过去的十年里，我一直在与一群国际合作者合作，以了解人类如何发展自我定位和自我运动的感觉。这里的关键是在环境中进行受控实验，在这些环境中，人类被呈现不寻常的提示组合，以阐明潜在的提示整合过程。随着最近长时间卧床休息的工作的完成，我帮助开发了一个深入而广泛的人类自我定位感知数据集，我计划用它来构建一个时间依赖的线索整合模型，该模型将使用水下机器人进行评估。 计划制定和执行：机器必须能够推理其环境以及如何对该环境采取行动。传统上，长期的机器人计划是使用基于逻辑结构分解任务的机制来开发的。尽管这些规划方法在当时很有效，但它们已经被现代人工智能方法（例如深度强化学习（Deep Reinforcement Learning，DRL））所取代，这些方法已经表现出非凡的能力，特别是对于那些可以进行适当训练的短期任务。我们如何才能最好地将从传统规划制度中吸取的经验教训与DRL等机制的能力结合起来？利用一组特定的任务-侵入式水厂监测和室内环境监测-我计划探索如何最好地使用DRL来学习短期计划，这些计划可以排序，为这些复杂但高度结构化的任务提供任务解决方案。人机交互（HRI）：机器必须能够与环境交互，最重要的是，它必须能够与占据其操作环境的人类交互。我的初步工作表明，任务特定的手势语言可以有效的HRI，但我们可以更好地利用底层的任务结构，以建立有效的HRI系统？利用常见的商业潜水员任务，我计划将我的初步工作概括为HRI表示中的语言标记和会话结构先验，并利用两者来理解人类与机器人的通信。