  Legged robots are becoming increasingly agile in exhibiting dynamic behaviors
such as running and jumping. Usually, such behaviors are either optimized and
engineered offline (i.e. the behavior is designed for before it is needed),
either through model-based trajectory optimization, or through deep
learning-based methods involving millions of timesteps of simulation
interactions. Notably, such offline-designed locomotion controllers cannot
perfectly model the true dynamics of the system, such as the motor dynamics. In
contrast, in this paper, we consider a quadruped jumping task that we rapidly
optimize online. We design foot force profiles parameterized by only a few
parameters which we optimize for directly on hardware with Bayesian
Optimization. The force profiles are tracked at the joint level, and added to
Cartesian PD impedance control and Virtual Model Control to stabilize the
jumping motions. After optimization, which takes only a handful of jumps, we
show that this control architecture is capable of diverse and omnidirectional
jumps including forward, lateral, and twist (turning) jumps, even on uneven
terrain, enabling the Unitree Go1 quadruped to jump 0.5 m high, 0.5 m forward,
and jump-turn over 2 rad. Video results can be found at
https://youtu.be/SvfVNQ90k_w.
