Long Horizon Task

Long-horizon tasks in robotics involve complex sequences of actions requiring planning and execution over extended periods, a significant challenge for current AI systems. Research focuses on integrating large language models (LLMs) with various planning algorithms (e.g., hierarchical planners, symbolic planners, model-based reinforcement learning) and vision-language models (VLMs) to generate and execute plans, often incorporating techniques like skill chaining and subgoal decomposition. This area is crucial for advancing robot autonomy in real-world applications, enabling robots to perform more complex and useful tasks in unstructured environments, such as household chores or industrial manipulation. Success hinges on improving the robustness, generalizability, and efficiency of these methods, particularly in handling uncertainty and partial observability.