Recent advances in machine learning techniques, including deep learning and hierarchical Bayesian modeling, are providing us with new possibilities to integrate high-level and low-level cognitive capabilities in robotics. Such a hierarchical integration of cognitive capabilities is required to enable a robot to use language to communicate and collaborate with people in the real-world environment. Because language is highly dependent on the context, this hierarchical integration in both bottom-up and top-down processes is also needed to make language processing reliable.
For instance, input data received by language learners is not written text data, but multimodal sensorimotor information including speech signal, haptic information, visual information, etc. Language learning strategies in real-world environments which are full of uncertainty would need to extract the best of multimodal information available. Making this learning and understanding of utterances possible, in a real-world environment with a situated and embodied system, is a key challenge for natural language processing. Moreover, humans are not only able to use language but also able to learn it. In order to have similar proficiency, robotic systems may have to learn it as well, probably not exactly in the same way, but it is rather unlikely that it would rely only on an ungrounded and disembodied language module identical to any robot.
This Research Topic is intended to provide an overview of the research from robotics, natural language processing, machine learning, and cognitive science to examine the challenges and opportunities emerging from the interdisciplinary research field covering language and robotics, and to share knowledge about the state-of-the-art machine learning methods that contribute to modeling language-related capabilities in robotics.
This Research Topic is based on outputs from the workshop
'Language and Robotics' at IROS 2018. However, we would also welcome spontaneous submissions not associated with this workshop, assuming they fit the scope of the Research Topic.
Topics of interest include, but are not limited to:
• Language acquisition by robots
• Symbol grounding/emergence in Robotics
• Multimodal communication
• Emergence of communication
• Learning complex motor skills and segmentation of time-series information
• Concept formation
• Probabilistic programming and reasoning in robotics
• Human-robot communication and collaboration based on machine learning
• Deep learning for robotics
• Bayesian modeling for high-level cognitive capabilities
• Application in communicable service robots
• Language understanding by Robots
• Neuro-inspired architectures for language processing
• Recurrent Neural Networks for online processing in robots