With the deepening of Industry 4.0, complex industrial processes show strong coupling, nonlinearity, and dynamic uncertainty, requiring coordinated optimization of production efficiency, quality, energy consumption, and emissions. Traditional control methods are often unable to balance conflicting objectives or adapt t...
This review examines AI-driven locomotion, including reinforcement learning, non-RL machine-learning methods, model-based approaches, and hybrid strategies, with attention to robustness, sim-to-real transfer, sensor use, computational requirements, and hardware validation, and summarizes AI-supported advanced behaviors...
Li-Kai Wu, C. Chow, W. Wong et al.· Frontiers in Neurorobotics· 0 citations
Experimental results demonstrate that the integrated system improves locomotion stability, energy efficiency, and terrain adaptability compared with baseline controllers, highlighting the effectiveness of combining a structured gait prior, lightweight residual coordination, and hardware-aware deployment for practical q...
