Contemporary robots have become exceptionally skilled at achieving specific tasks in structured environments. However, they often fail when faced with the limitless permutations of real-world unstructured environments. This motivates robotics methods which learn from experience, rather than follow a pre-defined set of rules. In this thesis, we present a range of learning-based methods aimed at enabling robots, operating in dynamic and unstructured environments, to better understand their surroundings, anticipate the actions of others, and take informed actions accordingly.

翻译：当代机器人已能出色地完成结构化环境中的特定任务，但在面对现实世界非结构化环境的无限变化时往往表现不佳。这促使机器人技术转向从经验中学习，而非遵循预设规则。本论文提出一系列基于学习的方法，旨在使在动态非结构化环境中运行的机器人能够更好地理解周围环境、预测其他智能体的行为，并据此采取智能决策。