Robot Multimodal Perception and Decision-Making Framework

With the rapid development of artificial intelligence and robotics, multimodal perception and decision-making have become crucial capabilities for robots to accomplish complex tasks. However, the integration of multimodal data and efficient decision-making still face challenges such as information redundancy, data heterogeneity, and insufficient real-time performance. This paper proposes an innovative multimodal perception and decision-making framework called MPDM, combining deep learning and reinforcement learning, through a unified representation learning mechanism and dynamic decision-making strategy, achieving efficient integration of multimodal data and intelligent decision-making. Specifically, we designed an adaptive attention module to dynamically allocate weights to visual, auditory, tactile, and other modal data, thereby enhancing the ability to extract key information. Additionally, we proposed a cross-modal consistency alignment algorithm, which enhances the information cooperative interaction between different modalities by embedding and aligning the feature spaces of multimodal data. In terms of decision optimization, we adopted a hybrid strategy combining reinforcement learning with modality-aware signals, significantly improving the decision-making efficiency and accuracy of robots in dynamic environments. Experimental results show that the framework demonstrates superior performance in task execution in both simulation and real-world scenarios, with decision accuracy improvements over existing baseline methods, showcasing excellent performance. This research provides new insights for constructing more intelligent and efficient robotic systems.