  Target-driven visual navigation aims at navigating an agent towards a given
target based on the observation of the agent. In this task, it is critical to
learn informative visual representation and robust navigation policy. Aiming to
improve these two components, this paper proposes three complementary
techniques, object relation graph (ORG), trial-driven imitation learning (IL),
and a memory-augmented tentative policy network (TPN). ORG improves visual
representation learning by integrating object relationships, including category
closeness and spatial correlations, e.g., a TV usually co-occurs with a remote
spatially. Both Trial-driven IL and TPN underlie robust navigation policy,
instructing the agent to escape from deadlock states, such as looping or being
stuck. Specifically, trial-driven IL is a type of supervision used in policy
network training, while TPN, mimicking the IL supervision in unseen
environment, is applied in testing. Experiment in the artificial environment
AI2-Thor validates that each of the techniques is effective. When combined, the
techniques bring significantly improvement over baseline methods in navigation
effectiveness and efficiency in unseen environments. We report 22.8% and 23.5%
increase in success rate and Success weighted by Path Length (SPL),
respectively. The code is available at
https://github.com/xiaobaishu0097/ECCV-VN.git.
