  In this work, a non-gaited framework for legged system locomotion is
presented. The approach decouples the gait sequence optimization by considering
the problem as a decision-making process. The redefined contact sequence
problem is solved by utilizing a Monte Carlo Tree Search (MCTS) algorithm that
exploits optimization-based simulations to evaluate the best search direction.
The proposed scheme has proven to have a good trade-off between exploration and
exploitation of the search space compared to the state-of-the-art Mixed-Integer
Quadratic Programming (MIQP). The model predictive control (MPC) utilizes the
gait generated by the MCTS to optimize the ground reaction forces and future
footholds position. The simulation results, performed on a quadruped robot,
showed that the proposed framework could generate known periodic gait and adapt
the contact sequence to the encountered conditions, including external forces
and terrain with unknown and variable properties. When tested on robots with
different layouts, the system has also shown its reliability.
