Language-Based Augmentation to Address Shortcut Learning in Object Goal Navigation

Deep Reinforcement Learning (DRL) has shown great potential in enabling robots to find certain objects (e.g., `find a fridge') in environments like homes or schools. This task is known as Object-Goal Navigation (ObjectNav). DRL methods are predominantly trained and evaluated using environment simulators. Although DRL has shown impressive results, the simulators may be biased or limited. This creates a risk of shortcut learning, i.e., learning a policy tailored to specific visual details of training environments. We aim to deepen our …

augmentation cs.cv cs.ro enabling environment environments homes language navigation objects reinforcement reinforcement learning robots schools shortcut