Long Horizon

Long-horizon tasks, encompassing extended sequences of actions towards a goal, are a central challenge in robotics and time series forecasting. Current research focuses on developing efficient model architectures, such as transformers with local attention mechanisms and novel state-space models inspired by neural network dynamics, to handle the computational complexity of long sequences. These advancements are improving the accuracy and speed of long-horizon prediction and planning in diverse applications, including robot manipulation, navigation, and weather forecasting. The resulting improvements in robustness and generalization capability are significant for deploying autonomous systems in complex, real-world scenarios.