About this Research Topic
To achieve Human-Level Artificial Intelligence, brain-inspired robotics need to reach a level of self-autonomy for carrying out tasks such as perception, affective & cognitive learning and memory, decision making, action and control. Hence multi-modal information processing technologies have been developed for acquiring, storing and transmitting information from heterogeneous sources such as distributed sensors, electroencephalograms, natural languages, social cognition, and so on. By doing this, brain-inspired robots are able to perceive and anticipate changes in their environment and engage in safe interactions with humans. Despite the significant progress of this promising approach, significant challenges remain for effectively exploiting multi-modal information fusion for brain-inspired robots in practical environments.
This Research Topic aims to provide a platform for bringing together multi-disciplinary research in innovative methodologies and applications of multi-modal information fusion for brain-inspired robots. In particular, we solicit reviews and original research works that aim to uncover real-world applications across diverse disciplines.
Topics of interest for this Research Topic include, but are not limited to:
- Novel methods and protocols for multi-modal information fusion in robots
- Brain-inspired information processing architectures
- Cognitive machine learning approaches
- Neural robotics applications
- Brain-computer interfaces
This Research Topic aims to provide a platform for bringing together multi-disciplinary research in innovative methodologies and applications of multi-modal information fusion for brain-inspired robots. In particular, we solicit reviews and original research works that aim to uncover real-world applications across diverse disciplines.
Topics of interest for this Research Topic include, but are not limited to:
- Novel methods and protocols for multi-modal information fusion in robots
- Brain-inspired information processing architectures
- Cognitive machine learning approaches
- Neural robotics applications
- Brain-computer interfaces
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
