  Autonomous systems, such as Unmanned Aerial Vehicles (UAVs), are expected to
run complex reinforcement learning (RL) models to execute fully autonomous
position-navigation-time tasks within stringent onboard weight and power
constraints. We observe that reducing onboard operating voltage can benefit the
energy efficiency of both the computation and flight mission, however, it can
also result in on-chip bit failures that are detrimental to mission safety and
performance. To this end, we propose BERRY, a robust learning framework to
improve bit error robustness and energy efficiency for RL-enabled autonomous
systems. BERRY supports robust learning, both offline and on-board the UAV, and
for the first time, demonstrates the practicality of robust low-voltage
operation on UAVs that leads to high energy savings in both compute-level
operation and system-level quality-of-flight. We perform extensive experiments
on 72 autonomous navigation scenarios and demonstrate that BERRY generalizes
well across environments, UAVs, autonomy policies, operating voltages and fault
patterns, and consistently improves robustness, efficiency and mission
performance, achieving up to 15.62% reduction in flight energy, 18.51% increase
in the number of successful missions, and 3.43x processing energy reduction.
