Locomotion Mode

Locomotion mode research focuses on enabling robots and assistive devices to smoothly transition between and execute various movement patterns, such as walking, jumping, or rolling, adapting to diverse terrains and tasks. Current research employs deep learning models, including neural networks and reinforcement learning algorithms, to achieve unified control across multiple locomotion modes, often leveraging sensor data like surface electromyography (sEMG) or inertial measurement units (IMUs) for real-time adaptation. This work is significant for advancing robotics in areas like legged locomotion, prosthetic control, and space exploration, improving the efficiency, robustness, and adaptability of robotic systems and assistive technologies.