  To achieve human-level dexterity, robots must infer spatial awareness from
multimodal sensing to reason over contact interactions. During in-hand
manipulation of novel objects, such spatial awareness involves estimating the
object's pose and shape. The status quo for in-hand perception primarily
employs vision, and restricts to tracking a priori known objects. Moreover,
visual occlusion of objects in-hand is imminent during manipulation, preventing
current systems to push beyond tasks without occlusion. We combine vision and
touch sensing on a multi-fingered hand to estimate an object's pose and shape
during in-hand manipulation. Our method, NeuralFeels, encodes object geometry
by learning a neural field online and jointly tracks it by optimizing a pose
graph problem. We study multimodal in-hand perception in simulation and the
real-world, interacting with different objects via a proprioception-driven
policy. Our experiments show final reconstruction F-scores of $81$% and average
pose drifts of $4.7\,\text{mm}$, further reduced to $2.3\,\text{mm}$ with known
CAD models. Additionally, we observe that under heavy visual occlusion we can
achieve up to $94$% improvements in tracking compared to vision-only methods.
Our results demonstrate that touch, at the very least, refines and, at the very
best, disambiguates visual estimates during in-hand manipulation. We release
our evaluation dataset of 70 experiments, FeelSight, as a step towards
benchmarking in this domain. Our neural representation driven by multimodal
sensing can serve as a perception backbone towards advancing robot dexterity.
Videos can be found on our project website
https://suddhu.github.io/neural-feels/
