  Despite their remarkable advancement in locomotion and manipulation, humanoid
robots remain challenged by a lack of synchronized loco-manipulation control,
hindering their full dynamic potential. In this work, we introduce a versatile
and effective approach to controlling and generalizing dynamic locomotion and
loco-manipulation on humanoid robots via a Force-and-moment-based Model
Predictive Control (MPC). Specifically, we proposed a simplified rigid body
dynamics (SRBD) model to take into account both humanoid and object dynamics
for humanoid loco-manipulation. This linear dynamics model allows us to
directly solve for ground reaction forces and moments via an MPC problem to
achieve highly dynamic real-time control. Our proposed framework is highly
versatile and generalizable. We introduce HECTOR (Humanoid for Enhanced ConTrol
and Open-source Research) platform to demonstrate its effectiveness in hardware
experiments. With the proposed framework, HECTOR can maintain exceptional
balance during double-leg stance mode, even when subjected to external force
disturbances to the body or foot location. In addition, it can execute 3-D
dynamic walking on a variety of uneven terrains, including wet grassy surfaces,
slopes, randomly placed wood slats, and stacked wood slats up to 6 cm high with
the speed of 0.6 m/s. In addition, we have demonstrated dynamic humanoid
loco-manipulation over uneven terrain, carrying 2.5 kg load. HECTOR
simulations, along with the proposed control framework, are made available as
an open-source project. (https://github.com/DRCL-USC/Hector_Simulation).
