Long Horizon Planning

Long-horizon planning focuses on enabling autonomous agents, particularly robots, to devise and execute complex plans spanning extended timeframes and involving multiple steps. Current research emphasizes developing robust and efficient algorithms, often leveraging deep learning architectures like diffusion models and value iteration networks, along with symbolic reasoning methods and large language models (LLMs) to handle uncertainty and complex constraints. This field is crucial for advancing robotics, autonomous systems, and AI, with applications ranging from navigation in unstructured environments to complex manipulation tasks and multi-agent coordination. The ultimate goal is to create agents capable of reliably achieving long-term goals in dynamic and unpredictable settings.