  Recent work on sim-to-real learning for bipedal locomotion has demonstrated
new levels of robustness and agility over a variety of terrains. However, that
work, and most prior bipedal locomotion work, have not considered locomotion
under a variety of external loads that can significantly influence the overall
system dynamics. In many applications, robots will need to maintain robust
locomotion under a wide range of potential dynamic loads, such as pulling a
cart or carrying a large container of sloshing liquid, ideally without
requiring additional load-sensing capabilities. In this work, we explore the
capabilities of reinforcement learning (RL) and sim-to-real transfer for
bipedal locomotion under dynamic loads using only proprioceptive feedback. We
show that prior RL policies trained for unloaded locomotion fail for some loads
and that simply training in the context of loads is enough to result in
successful and improved policies. We also compare training specialized policies
for each load versus a single policy for all considered loads and analyze how
the resulting gaits change to accommodate different loads. Finally, we
demonstrate sim-to-real transfer, which is successful but shows a wider
sim-to-real gap than prior unloaded work, which points to interesting future
research.
