  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.
