  This paper presents a novel learning-based control framework that uses
keyframing to incorporate high-level objectives in natural locomotion for
legged robots. These high-level objectives are specified as a variable number
of partial or complete pose targets that are spaced arbitrarily in time. Our
proposed framework utilizes a multi-critic reinforcement learning algorithm to
effectively handle the mixture of dense and sparse rewards. Additionally, it
employs a transformer-based encoder to accommodate a variable number of input
targets, each associated with specific time-to-arrivals. Throughout simulation
and hardware experiments, we demonstrate that our framework can effectively
satisfy the target keyframe sequence at the required times. In the experiments,
the multi-critic method significantly reduces the effort of hyperparameter
tuning compared to the standard single-critic alternative. Moreover, the
proposed transformer-based architecture enables robots to anticipate future
goals, which results in quantitative improvements in their ability to reach
their targets.
