  We tackle the recently introduced benchmark for whole-body humanoid control
HumanoidBench using MuJoCo MPC. We find that sparse reward functions of
HumanoidBench yield undesirable and unrealistic behaviors when optimized;
therefore, we propose a set of regularization terms that stabilize the robot
behavior across tasks. Current evaluations on a subset of tasks demonstrate
that our proposed reward function allows achieving the highest HumanoidBench
scores while maintaining realistic posture and smooth control signals. Our code
is publicly available and will become a part of MuJoCo MPC, enabling rapid
prototyping of robot behaviors.
