Multi Object Navigation

Multi-object navigation (MON) focuses on enabling robots to efficiently locate and navigate to multiple objects within complex environments, mirroring real-world tasks like household chores or warehouse operations. Current research emphasizes improving efficiency through methods like coarse-to-fine exploration strategies, reusable open-vocabulary feature maps, and cooperative multi-agent systems often incorporating large language models for communication and planning. These advancements leverage various architectures, including transformers, deep reinforcement learning, and hybrid approaches combining classical planning with deep learning, aiming to enhance both the speed and robustness of MON in both simulated and real-world settings. The resulting improvements in robotic navigation have significant implications for various fields, including assistive robotics, logistics, and exploration.