  Reinforcement learning (RL) has become a promising approach to developing
controllers for quadrupedal robots. Conventionally, an RL design for locomotion
follows a position-based paradigm, wherein an RL policy outputs target joint
positions at a low frequency that are then tracked by a high-frequency
proportional-derivative (PD) controller to produce joint torques. In contrast,
for the model-based control of quadrupedal locomotion, there has been a
paradigm shift from position-based control to torque-based control. In light of
the recent advances in model-based control, we explore an alternative to the
position-based RL paradigm, by introducing a torque-based RL framework, where
an RL policy directly predicts joint torques at a high frequency, thus
circumventing the use of a PD controller. The proposed learning torque control
framework is validated with extensive experiments, in which a quadruped is
capable of traversing various terrain and resisting external disturbances while
following user-specified commands. Furthermore, compared to learning position
control, learning torque control demonstrates the potential to achieve a higher
reward and is more robust to significant external disturbances. To our
knowledge, this is the first sim-to-real attempt for end-to-end learning torque
control of quadrupedal locomotion.
