Gait Design

Gait design focuses on creating efficient and adaptable locomotion patterns for robots, mimicking natural movements and enabling navigation in diverse environments. Current research emphasizes developing robust algorithms, such as Bayesian optimization and cross-attention mechanisms, to optimize gait parameters in real-time, often incorporating multimodal sensor data (visual, inertial) and leveraging model architectures like Vision Transformers and convolutional neural networks. This work is significant for improving robot mobility in challenging terrains and for developing advanced diagnostic tools for human gait analysis, with applications ranging from assistive robotics to rehabilitation.