  Implicit representations such as Neural Radiance Fields (NeRF) have been
shown to be very effective at novel view synthesis. However, these models
typically require manual and careful human data collection for training. In
this paper, we present AutoNeRF, a method to collect data required to train
NeRFs using autonomous embodied agents. Our method allows an agent to explore
an unseen environment efficiently and use the experience to build an implicit
map representation autonomously. We compare the impact of different exploration
strategies including handcrafted frontier-based exploration, end-to-end and
modular approaches composed of trained high-level planners and classical
low-level path followers. We train these models with different reward functions
tailored to this problem and evaluate the quality of the learned
representations on four different downstream tasks: classical viewpoint
rendering, map reconstruction, planning, and pose refinement. Empirical results
show that NeRFs can be trained on actively collected data using just a single
episode of experience in an unseen environment, and can be used for several
downstream robotic tasks, and that modular trained exploration models
outperform other classical and end-to-end baselines. Finally, we show that
AutoNeRF can reconstruct large-scale scenes, and is thus a useful tool to
perform scene-specific adaptation as the produced 3D environment models can be
loaded into a simulator to fine-tune a policy of interest.
