Multi Goal

Multi-goal research focuses on enabling agents, particularly robots and AI systems, to effectively achieve multiple, often interdependent, objectives simultaneously. Current research emphasizes developing efficient planning algorithms, including neuro-symbolic approaches combining the strengths of symbolic planners and large language models, and reinforcement learning methods like model-based offline goal-conditioned RL. These advancements aim to improve task completion rates, reduce planning time, and enhance robustness in complex and uncertain environments, with applications ranging from robotics and autonomous navigation to multi-agent collaboration and human-computer interaction. The ultimate goal is to create more adaptable and capable intelligent systems.