Monte Carlo Tree Search Gait Planner for Non-Gaited Legged System Control

TL;DR

This paper introduces a non-gaited locomotion framework for legged robots using Monte Carlo Tree Search to optimize contact sequences, enabling adaptive and reliable gait generation across various terrains and robot configurations.

Contribution

The work presents a novel MCTS-based approach for contact sequence planning that outperforms traditional MIQP methods in exploration and exploitation balance.

Findings

Successfully generates periodic gait patterns in simulation.

Adapts contact sequences to external forces and variable terrains.

Demonstrates robustness across different robot layouts.

Abstract

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.