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

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。

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.