Reinforcement Learning for Semantic Segmentation in Indoor Scenes

Abstract

Future advancements in robot autonomy and sophistication of robotics tasks rest on robust, efficient, and task-dependent semantic understanding of the environment. Semantic segmentation is the problem of simultaneous segmentation and categorization of a partition of sensory data. The majority of current approaches tackle this using multi-class segmentation and labeling in a Conditional Random Field (CRF) framework or by generating multiple object hypotheses and combining them sequentially. In practical settings, the subset of semantic labels that are needed depend on the task and particular scene and labelling every single pixel is not always necessary. We pursue these observations in developing a more modular and flexible approach to multi-class parsing of RGBD data based on learning strategies for combining independent binary object-vs-background segmentations in place of the usual monolithic multi-label CRF approach. Parameters for the independent binary segmentation models can be learned very efficiently, and the combination strategy---learned using reinforcement learning---can be set independently and can vary over different tasks and environments. Accuracy is comparable to state-of-art methods on a subset of the NYU-V2 dataset of indoor scenes, while providing additional flexibility and modularity.