Continuous Environment

Continuous environment research focuses on developing algorithms and models enabling agents to navigate and interact within unbounded, realistically complex 3D spaces, often guided by natural language instructions or other high-level goals. Current research emphasizes robust methods for multi-agent pathfinding, collision avoidance, and efficient exploration strategies, often leveraging large language models, graph-based representations, and reinforcement learning techniques like DDPG and model predictive control. This field is crucial for advancing embodied AI, with applications ranging from autonomous robotics and navigation to human-robot interaction and complex industrial control systems.