Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
This paper presents a novel approach to intelligent robot control by leveraging the fusion of multiple modalities – visual, tactile, and auditory – of information. The core idea is to enhance robot adaptability and intelligence in complex environments through sophisticated multi-modal data processing and intelligent control strategy learning. We employ deep learning techniques for the initial feature extraction and fusion from each modality, followed by reinforcement learning to train the robot's control policy. The proposed system aims to achieve a higher level of robot perception and action, bridging the gap between raw sensory input and effective robotic behavior. The system is designed for adaptability to varying environmental conditions and task requirements. The key contribution lies in the integrated architecture and the utilization of deep learning for robust multi-modal feature representation and reinforcement learning for adaptive control. The efficacy of the approach is demonstrated through a theoretical framework and a conceptual design.
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