Navigation Task
Navigation tasks in robotics aim to enable robots to move efficiently and safely through various environments, often while completing specific objectives like object retrieval or reaching designated locations. Current research emphasizes robust navigation in complex, dynamic settings, incorporating social awareness, handling ambiguous instructions (often via large language models), and improving sample efficiency in reinforcement learning. These advancements are crucial for deploying robots in real-world scenarios, such as assistance in homes, warehouses, or disaster relief, and are driving innovation in areas like perception, planning, and control algorithms.
Papers
Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games
Stephanie Milani, Arthur Juliani, Ida Momennejad, Raluca Georgescu, Jaroslaw Rzpecki, Alison Shaw, Gavin Costello, Fei Fang, Sam Devlin, Katja Hofmann
Subgoal-Driven Navigation in Dynamic Environments Using Attention-Based Deep Reinforcement Learning
Jorge de Heuvel, Weixian Shi, Xiangyu Zeng, Maren Bennewitz