For agents operating in the real world, spatial reasoning and understanding the spatial properties of the environment are important abilities for the execution of tasks related to spatial movement. Agents can perform various tasks by acquiring and using the semantic and linguistic knowledge related to place and the object locations. Current research on spatial reasoning and semantic understanding in robots is important as an element to realize self-localization with uncertainty in the real world and planning with human robot interaction. It is also very closely related as a constructive approach to brain-inspired AI related to spatial cognition represented by the hippocampal formation.
In this research topic, we would like to address the interdisciplinary fusion of the knowledge of artificial intelligence, robotics, cognitive development, and neuroscience in spatial cognition and spatial reasoning.
For example, in the fusion area of natural language processing and computer vision, research on vision-and-language navigation (VLN) has been recently implemented. However, there are still few studies applying VLN technology to real robots in the real world. As a future prospect, a VLN that operates in a real environment is required.
Additionally, in robotics AI, it would be useful to refer to the cognitive and neuroscientific findings of concept formation related to place and spatial language acquisition.
To achieve the above, a constructive approach with robots operating in the real world would be effective.
This research topic widely welcomes from fundamental to applied research, which related to spatial reasoning using robots and semantic understanding including language interaction, in the fusion area of artificial intelligence such as machine learning, robotics, and computational neuroscience.
We encourage contributions on a technical basis, e.g., semantic SLAM, place recognition, and, navigation, for performing tasks including spatial movement. Additionally, we look forward to contributions on computational models related to spatial reasoning, such as refer to the hippocampal formation and spatial cognitive capabilities. The focus is also on contributions on cutting-edge machine learning for use in the above.
For agents operating in the real world, spatial reasoning and understanding the spatial properties of the environment are important abilities for the execution of tasks related to spatial movement. Agents can perform various tasks by acquiring and using the semantic and linguistic knowledge related to place and the object locations. Current research on spatial reasoning and semantic understanding in robots is important as an element to realize self-localization with uncertainty in the real world and planning with human robot interaction. It is also very closely related as a constructive approach to brain-inspired AI related to spatial cognition represented by the hippocampal formation.
In this research topic, we would like to address the interdisciplinary fusion of the knowledge of artificial intelligence, robotics, cognitive development, and neuroscience in spatial cognition and spatial reasoning.
For example, in the fusion area of natural language processing and computer vision, research on vision-and-language navigation (VLN) has been recently implemented. However, there are still few studies applying VLN technology to real robots in the real world. As a future prospect, a VLN that operates in a real environment is required.
Additionally, in robotics AI, it would be useful to refer to the cognitive and neuroscientific findings of concept formation related to place and spatial language acquisition.
To achieve the above, a constructive approach with robots operating in the real world would be effective.
This research topic widely welcomes from fundamental to applied research, which related to spatial reasoning using robots and semantic understanding including language interaction, in the fusion area of artificial intelligence such as machine learning, robotics, and computational neuroscience.
We encourage contributions on a technical basis, e.g., semantic SLAM, place recognition, and, navigation, for performing tasks including spatial movement. Additionally, we look forward to contributions on computational models related to spatial reasoning, such as refer to the hippocampal formation and spatial cognitive capabilities. The focus is also on contributions on cutting-edge machine learning for use in the above.
