About this Research Topic
Robot perception and cognition often rely on the integration of information from multiple sensory modalities, such as vision, touch, and proprioception. Large-scale neural network models have demonstrated significant advancements in processing unimodal sensory data. However, efficiently combining and leveraging information from multiple modalities remains a challenge. Multi-modal learning aims to develop computational models capable of jointly processing and integrating heterogeneous sensory inputs to facilitate a more robust and comprehensive understanding of the environment.
The goal of this research topic is to investigate techniques for multi-modal learning using large-scale neural network models in the context of neurorobotics. By harnessing the complementary information provided by different sensory modalities, we aim to enhance the perceptual capabilities of robots, enabling them to perceive, interpret, and interact with their surroundings more effectively. Our objective is to develop algorithms and architectures that can seamlessly fuse multi-modal sensory inputs to improve tasks such as object recognition, localization, navigation, and manipulation in robotic systems.
We welcome submissions that contribute to advancing the field of multi-modal learning with large-scale models in neurorobotics. Authors are encouraged to present original research, reviews, methodologies, and case studies that elucidate novel techniques, architectures, and applications in multi-modal perception for robotic systems. Submissions should provide detailed descriptions of the proposed methodologies, experimental setups, and empirical evaluations, along with insights into the practical implications and potential impact of the research on real-world robotic applications. Additionally, authors are invited to discuss challenges, limitations, and future directions for further advancing multi-modal learning in neurorobotics.
We welcome the following themes, but not limited to:
- Exploration of fusion strategies for integrating information from diverse sensory modalities, including early fusion, late fusion, and hierarchical fusion approaches.
- Development of large-scale neural network architectures capable of accommodating multi-modal inputs, such as multi-stream networks, attention mechanisms, and graph neural networks.
- Investigation of domain adaptation and transfer learning techniques for leveraging pre-trained large-scale models across different sensory modalities and robotic platforms.
- Integration of uncertainty estimation methods to account for sensor noise, calibration errors, and missing modalities in multi-modal perception systems.
- Study of active sensing and sensorimotor control strategies that leverage multi-modal feedback for adaptive behavior generation and task execution in dynamic environments.
- Evaluation methodologies for assessing the performance, robustness, and generalization capabilities of multi-modal learning approaches in realistic robotic scenarios, including benchmarks and simulation environments.
The goal of this research topic is to investigate techniques for multi-modal learning using large-scale neural network models in the context of neurorobotics. By harnessing the complementary information provided by different sensory modalities, we aim to enhance the perceptual capabilities of robots, enabling them to perceive, interpret, and interact with their surroundings more effectively. Our objective is to develop algorithms and architectures that can seamlessly fuse multi-modal sensory inputs to improve tasks such as object recognition, localization, navigation, and manipulation in robotic systems.
We welcome submissions that contribute to advancing the field of multi-modal learning with large-scale models in neurorobotics. Authors are encouraged to present original research, reviews, methodologies, and case studies that elucidate novel techniques, architectures, and applications in multi-modal perception for robotic systems. Submissions should provide detailed descriptions of the proposed methodologies, experimental setups, and empirical evaluations, along with insights into the practical implications and potential impact of the research on real-world robotic applications. Additionally, authors are invited to discuss challenges, limitations, and future directions for further advancing multi-modal learning in neurorobotics.
We welcome the following themes, but not limited to:
- Exploration of fusion strategies for integrating information from diverse sensory modalities, including early fusion, late fusion, and hierarchical fusion approaches.
- Development of large-scale neural network architectures capable of accommodating multi-modal inputs, such as multi-stream networks, attention mechanisms, and graph neural networks.
- Investigation of domain adaptation and transfer learning techniques for leveraging pre-trained large-scale models across different sensory modalities and robotic platforms.
- Integration of uncertainty estimation methods to account for sensor noise, calibration errors, and missing modalities in multi-modal perception systems.
- Study of active sensing and sensorimotor control strategies that leverage multi-modal feedback for adaptive behavior generation and task execution in dynamic environments.
- Evaluation methodologies for assessing the performance, robustness, and generalization capabilities of multi-modal learning approaches in realistic robotic scenarios, including benchmarks and simulation environments.
Keywords: Multi-modal Learning, Neural Network Models, Neurorobotics
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.
