Enabling rich, open-ended human-robot interaction through robust, advanced multimodal perceptual capabilities for high-level reasoning

通过强大、先进的多模态感知能力进行高级推理，实现丰富、开放式的人机交互

• 批准号: RGPIN-2019-06047
• 负责人: Ferland, François
• 金额: $ 1.68万
• 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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=679235
• 关键词: Enabling; rich; open; ended; human

The ultimate goal in Human-Robot Interaction is to have autonomous robots that can naturally interact with people, assist them in their daily lives at home or at work, and automatically adapt to new situations. Building such robots brings a great number of scientific challenges: safe, natural and effective interaction implies advanced perceptual capabilities to supply information for sound and robust reasoning. As humans operate in dynamic environments, robots will need a broad understanding of the world we live in and reasoning capabilities outside of the usually rigid, pre-defined scenarios they are currently programmed for.******My research program is oriented toward enabling rich, open-ended interactions between humans and autonomous mobile robots. Doing so require high-level reasoning, which I believe is only possible with large-scale and multimodal perceptual capabilities on robots. To provide such capabilities, and as identified by studies on selective attention (SA) in neurosciences, there is growing evidence that top-down modulation occurs in the brain to maintain performance of the working memory by prioritizing already encoded items depending on the changing tasks demands and expectations of dynamic environments. A robot meant for rich interaction will have to adapt itself to such environments and prioritize what needs to be encoded in its own working memory. Furthermore, it will have to anticipate perceptual requirements from long-term memories of past experiences and manage its computing resources for future expected stimuli.******To study such mechanisms, I will build a framework for distributed perceptual processing at a large scale based on existing open-source technologies to combine the stimuli of multiple stationary and mobile robot-embedded sensors and balancing the resources of multiple computing systems. This will be done to investigate how a robot can build multimodal understandings of their surroundings. Then, an SA-inspired mechanism will also filter the percepts encoded in both working and long-term memories to avoid overloading computing resources. Finally, to go beyond reactive resource management and prepare for expected future events, an anticipatory supervision mechanism will be designed to infer which stimuli to expect from past experiences.******Validation of this research program will be done with wheeled humanoid robots with advanced manipulation and sensing capabilities in realistic settings. Participating to an international competition such as RoboCup@Home is planned to serve as common ground for evaluation of the added value of the mechanisms developed in relation to state-of-the-art in the field. ******This research program represents a unique opportunity to study embodied artificial intelligence (AI) to respond and anticipate to events in real interactive environments with humans. The program will include multiple projects for MSc and PhD students to train them as experts in applied AI and robotics.*****

人机交互的最终目标是拥有能够与人自然互动的自主机器人，帮助他们在家庭或工作中的日常生活，并自动适应新的情况。制造这样的机器人带来了大量的科学挑战：安全、自然和有效的交互意味着先进的感知能力，为健全和稳健的推理提供信息。随着人类在动态环境中工作，机器人将需要对我们生活的世界有广泛的了解，并需要在它们目前编程的通常严格的、预先定义的场景之外有推理能力。******我的研究项目旨在实现人类和自主移动机器人之间丰富的开放式互动。这样做需要高水平的推理，我相信这只有通过机器人的大规模和多模态感知能力才能实现。为了提供这样的能力，正如神经科学中选择性注意（SA）的研究所确定的那样，越来越多的证据表明，大脑中发生了自上而下的调节，通过根据不断变化的任务需求和动态环境的期望对已经编码的项目进行优先排序来维持工作记忆的表现。一个旨在进行丰富互动的机器人将不得不适应这样的环境，并优先考虑需要在自己的工作记忆中编码的内容。此外，它必须从过去经验的长期记忆中预测感知需求，并管理其计算资源以应对未来预期的刺激。******为了研究这种机制，我将基于现有的开源技术，构建一个大规模分布式感知处理框架，将多个固定和移动机器人嵌入式传感器的刺激组合起来，平衡多个计算系统的资源。这将用于研究机器人如何建立对周围环境的多模态理解。然后，sa启发的机制也将过滤编码在工作记忆和长期记忆中的感知，以避免计算资源过载。最后，为了超越反应性资源管理并为预期的未来事件做好准备，将设计一种预期监督机制，以从过去的经验中推断出预期的刺激。******该研究计划的验证将在现实环境中使用具有先进操作和传感能力的轮式人形机器人进行。计划参加诸如RoboCup@Home之类的国际竞赛，以作为评价与该领域最新技术有关的机制的附加价值的共同基础。******这个研究项目代表了一个独特的机会来研究具体的人工智能（AI），以响应和预测与人类真实互动环境中的事件。该项目将为硕士和博士学生提供多个项目，以培养他们成为应用人工智能和机器人领域的专家。*****