The level of decision-making intelligence of heavy-duty hexapod robots in the field terrain needs to be improved. However, if robots have not yet formed a reasonable decision structure level, the conventional decision-making reinforcement learning which is directly interact with the environment, will lead to the robot′s decision-making being too divergent. Therefore, this article first obtains the driver′s decision-making experience model through a step-training neural network which conforms to the driver′s decision-making habits. Hence, the robot can quickly form decision-making intelligence. In addition, to better play the advantages of human-robot decision-making, this article proposes a method to eliminate the conflict of human-robot coordinated decision-making commands based on the cooperative game theory. A semi-physical simulation experiment system for human-machine collaborative decision-making of heavyduty hexapod robots is designed and established. After carrying out experimental verification around the proposed methods, results show that the robot can approach the driver decision-making effect by learning the driver′s prior model and reinforcement training, and the effect of the human-robot collaborative decision-making commands can also make up for the defects in unilateral decision-making. In the regular ditches terrain, the collision index of the collaborative decision commands is 23. 8% better than that of the single driver agent commands; in the obstacle terrain, the energy consumption index of the collaborative decision commands is better than that of the single robot agent commands by 34. 1% .