Integrating multi-level sensory-motor and cognitive capabilities is essential for developing robotic systems that can adaptively act in our daily environment in active collaboration with humans. In this research topic, we aim to share knowledge about the state-of-the-art machine learning methods that contribute to modeling sensory-motor and cognitive capabilities in robotics with a special emphasis on adaptive high-level cognition.
Our daily environment is full of uncertainties with complex objects and challenging tasks. A robot is not only required to deal with things appropriately in a physical manner but also required to perform linguistic and/or logical tasks in the real world. When a robot attempts to communicate and collaborate with human users in a real-world environment, e.g. the RoboCup@Home environment, bridging high-level and low-level cognitive capabilities appropriately is crucial. The high-level cognitive capabilities include logical inference, planning, and language. In contrast, the low-level cognitive capability includes physical control, behavioral motion generation and sensory perception.
Conventionally, symbol-based and/or rule-based approaches have been employed to model high-level cognitive capabilities in robotics. However, it has been pointed out that such conventional methods could not deal with the uncertainty that is inevitably found in the physical environment and natural human-robot communication.
Recent advances in machine learning methods, including deep learning and hierarchical Bayesian modeling, are providing us with new possibilities to integrate high-level and low-level cognitive capabilities in robotics. It became clear that such learning methods are indispensable to create robots that can effectively deal with uncertainty in the real world.
This research topic includes multimodal communication, emergence of communication, learning motor skills, segmentation of time-series information, concept formation, probabilistic programing and reasoning in robotics, language acquisition, human-robot communication and collaboration based on machine learning, deep learning for robotics, and Bayesian modeling for high-level cognitive capabilities. Note that the list is non-exhaustive. This research topic is closely related to the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016 held at the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016). We welcome not only the papers presented at the workshop, but also new papers related to the research topic.
According to this, the Research topic welcomes various articles that contribute to the progress in machine learning methods for high-level cognitive capabilities in robotics.
Integrating multi-level sensory-motor and cognitive capabilities is essential for developing robotic systems that can adaptively act in our daily environment in active collaboration with humans. In this research topic, we aim to share knowledge about the state-of-the-art machine learning methods that contribute to modeling sensory-motor and cognitive capabilities in robotics with a special emphasis on adaptive high-level cognition.
Our daily environment is full of uncertainties with complex objects and challenging tasks. A robot is not only required to deal with things appropriately in a physical manner but also required to perform linguistic and/or logical tasks in the real world. When a robot attempts to communicate and collaborate with human users in a real-world environment, e.g. the RoboCup@Home environment, bridging high-level and low-level cognitive capabilities appropriately is crucial. The high-level cognitive capabilities include logical inference, planning, and language. In contrast, the low-level cognitive capability includes physical control, behavioral motion generation and sensory perception.
Conventionally, symbol-based and/or rule-based approaches have been employed to model high-level cognitive capabilities in robotics. However, it has been pointed out that such conventional methods could not deal with the uncertainty that is inevitably found in the physical environment and natural human-robot communication.
Recent advances in machine learning methods, including deep learning and hierarchical Bayesian modeling, are providing us with new possibilities to integrate high-level and low-level cognitive capabilities in robotics. It became clear that such learning methods are indispensable to create robots that can effectively deal with uncertainty in the real world.
This research topic includes multimodal communication, emergence of communication, learning motor skills, segmentation of time-series information, concept formation, probabilistic programing and reasoning in robotics, language acquisition, human-robot communication and collaboration based on machine learning, deep learning for robotics, and Bayesian modeling for high-level cognitive capabilities. Note that the list is non-exhaustive. This research topic is closely related to the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics 2016 held at the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016). We welcome not only the papers presented at the workshop, but also new papers related to the research topic.
According to this, the Research topic welcomes various articles that contribute to the progress in machine learning methods for high-level cognitive capabilities in robotics.
