  The current state-of-the-art gradient-based optimisation frameworks are able
to produce impressive dynamic manoeuvres such as linear and rotational jumps.
However, these methods, which optimise over the full rigid-body dynamics of the
robot, often require precise foothold locations apriori, while real-time
performance is not guaranteed without elaborate regularisation and tuning of
the cost function. In contrast, we investigate the advantages of a task-space
optimisation framework, with special focus on acrobatic motions. Our proposed
formulation exploits the system's high-order nonlinearities, such as the
nonholonomy of the angular momentum, in order to produce feasible,
high-acceleration manoeuvres. By leveraging the full-centroidal dynamics of the
quadruped ANYmal C and directly optimising its footholds and contact forces,
the framework is capable of producing efficient motion plans with low
computational overhead. Finally, we deploy our proposed framework on the ANYmal
C platform, and demonstrate its true capabilities through real-world
experiments, with the successful execution of high-acceleration motions, such
as linear and rotational jumps. Extensive analysis of these shows that the
robot's dynamics can be exploited to surpass its hardware limitations of having
a high mass and low-torque limits.
