Long Horizon Manipulation Task

Long-horizon manipulation tasks challenge robots to perform complex, multi-step actions requiring sequential reasoning and robust execution. Current research focuses on improving the efficiency and reliability of these tasks through methods like hierarchical reinforcement learning, imitation learning from diverse sources (including human demonstrations and play data), and model-based planning integrated with learned components. These advancements leverage various architectures, including large language models, dynamic movement primitives, and neural networks, to address challenges such as generalization to unseen objects and environments, efficient planning in high-dimensional spaces, and robust handling of uncertainties. The resulting improvements in robotic dexterity and adaptability have significant implications for automating complex tasks in manufacturing, logistics, and domestic settings.