Motor Adaptation

Motor adaptation research focuses on how biological and artificial systems adjust their movements in response to changing environments or tasks, aiming to improve performance and robustness. Current research emphasizes developing algorithms and models, including reinforcement learning approaches like model-based deterministic policy gradients and three-factor learning rules within spiking neural networks, to enable rapid and effective adaptation in robots and other systems. This work draws inspiration from biological motor control mechanisms and is crucial for advancing robotics, particularly in areas like manipulation and locomotion, where adaptability is essential for real-world applications. Furthermore, comparing these models to human motor learning provides valuable insights into the underlying neural processes.