  Operation of Autonomous Mobile Robots (AMRs) of all forms that include
wheeled ground vehicles, quadrupeds and humanoids in dynamically changing GPS
denied environments without a-priori maps, exclusively using onboard sensors,
is an unsolved problem that has potential to transform the economy, and vastly
improve humanity's capabilities with improvements to agriculture,
manufacturing, disaster response, military and space exploration. Conventional
AMR automation approaches are modularized into perception, motion planning and
control which is computationally inefficient, and requires explicit feature
extraction and engineering, that inhibits generalization, and deployment at
scale. Few works have focused on real-world end-to-end approaches that directly
map sensor inputs to control outputs due to the large amount of well curated
training data required for supervised Deep Learning (DL) which is time
consuming and labor intensive to collect and label, and sample inefficiency and
challenges to bridging the simulation to reality gap using Deep Reinforcement
Learning (DRL). This paper presents a novel method to efficiently train DRL for
robust end-to-end AMR exploration, in a constrained environment at physical
limits in simulation, transferred zero-shot to the real-world. The
representation learned in a compact parameter space with 2 fully connected
layers with 64 nodes each is demonstrated to exhibit emergent behavior for
out-of-distribution generalization to navigation in new environments that
include unstructured terrain without maps, and dynamic obstacle avoidance. The
learned policy outperforms conventional navigation algorithms while consuming a
fraction of the computation resources, enabling execution on a range of AMR
forms with varying embedded computer payloads.
