Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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IEEE transactions on cybernetics
Knowledge Distillation and Reinforcement Learning in a Human–Machine Collaboration Delivery System With a Robotic Arm
Robotic arms are widely used in various aspects of human-robot collaboration. The primary goal of this study is to explore the usability of robotic arms for delivering objects to humans in dynamic environments. Traditional robotic arms often face limitations in path planning, such as difficulties adapting to dynamic environments and complex developmental processes. To overcome these challenges, this study employs reinforcement learning (RL) to train four models-the Approach RL Model, Delivery RL Model, Decision RL Model, and Merged Model-as alternatives to conventional path planning control. Typically, there exists a significant discrepancy between simulated data and real-world features. Although image segmentation can substantially reduce the gap between virtual and real environments, notable differences remain in hand features. Therefore, to further bridge the simulation-to-reality gap, this study applies CycleGAN to transform real hand features into virtual hand features, thereby enhancing the model's transferability. Experimental results show that the Decision RL Model achieved an accuracy of 99.17%, while the Merged Model achieved 99.92%. The proposed method effectively improves the stability and accuracy of human-robot collaboration in complex scenarios. Overall, this study validates the feasibility of integrating RL, image segmentation, and image translation techniques, offering a scalable and efficient task-solving solution for robotic arms in highly dynamic application domains.
3 Aug 2026
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Multimodal emotion recognition using hybrid deep feature fusion under speaker-independent evaluation
Emotion recognition is one of the most important and complex challenges for machines to understand, as most robots and AI agents struggle with human-centric perception and interpretation. Therefore, this paper introduces a novel multimodal emotion recognition system that analyzes emotions through two complementary channels: voice and facial expressions. The proposed approach is evaluated on the RAVDESS and CREMA-D datasets, which consist of acted emotional expressions across multiple discrete emotion categories. Utilizing an advanced multimodal deep feature fusion technique, the system combines handcrafted audio features (e.g., Mel-Frequency Cepstral Coefficients (MFCCs)) with deep visual features extracted from an attention-based VGGFace model. These features are integrated into a unified representation through a hybrid fusion strategy that jointly employs concatenation, cross-attention, gated fusion, and multiplicative fusion mechanisms to capture complementary cross-modal interactions. To ensure a comprehensive and realistic assessment, the model is evaluated under both random-split and strict speaker-independent protocols. On the RAVDESS dataset, the proposed system achieves an accuracy of 95.83% under random-split evaluation and 48.06% ± 9.76% accuracy under speaker-independent Leave-One-Speaker-Out (LOSO) testing, while on the CREMA-D dataset it attains 73.54% accuracy using random splits and 53.12% ± 2.65% accuracy under subject-exclusive speaker-independent 5-fold cross-validation.
27 June 2026
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