  Recent advances in deep learning have led to a data-centric intelligence i.e.
artificially intelligent models unlocking the potential to ingest a large
amount of data and be really good at performing digital tasks such as
text-to-image generation, machine-human conversation, and image recognition.
This thesis covers the topic of learning with structured inductive bias and
priors to design approaches and algorithms unlocking the potential of
principle-centric intelligence. Prior knowledge (priors for short), often
available in terms of past experience as well as assumptions of how the world
works, helps the autonomous agent generalize better and adapt their behavior
based on past experience. In this thesis, I demonstrate the use of prior
knowledge in three different robotics perception problems. 1. object-centric 3D
reconstruction, 2. vision and language for decision-making, and 3. 3D scene
understanding. To solve these challenging problems, I propose various sources
of prior knowledge including 1. geometry and appearance priors from synthetic
data, 2. modularity and semantic map priors and 3. semantic, structural, and
contextual priors. I study these priors for solving robotics 3D perception
tasks and propose ways to efficiently encode them in deep learning models. Some
priors are used to warm-start the network for transfer learning, others are
used as hard constraints to restrict the action space of robotics agents. While
classical techniques are brittle and fail to generalize to unseen scenarios and
data-centric approaches require a large amount of labeled data, this thesis
aims to build intelligent agents which require very-less real-world data or
data acquired only from simulation to generalize to highly dynamic and
cluttered environments in novel simulations (i.e. sim2sim) or real-world unseen
environments (i.e. sim2real) for a holistic scene understanding of the 3D
world.
