Qualitative modeling allows autonomous agents to learn comprehensible control models, formulated in a way that is close to human intuition. By abstracting away certain numerical information, qualitative models can provide better insights into operating principles of a dynamic system in comparison to traditional numerical models. We show that qualitative models, learned from numerical traces, contain enough information to allow motion planning and path following. We demonstrate our methods on the task of flying a quadcopter. A qualitative control model is learned through motor babbling. Training is significantly faster than training times reported in papers using reinforcement learning with similar quadcopter experiments. A qualitative collision-free trajectory is computed by means of qualitative simulation, and executed reactively while dynamically adapting to numerical characteristics of the system. Experiments have been conducted and assessed in the V-REP robotic simulator.

定性建模使自主代理可以学习易于理解的控制模型，该模型以接近人类直觉的方式制定。通过提取某些数值信息，与传统的数值模型相比，定性模型可以更好地了解动态系统的工作原理。我们表明，从数字轨迹中学习到的定性模型包含足够的信息，可以进行运动规划和路径跟踪。我们演示了在飞行四轴飞行器任务上的方法。通过电机行学习定性控制模型。与采用类似四轴飞行器实验的强化学习相比，训练速度明显快于论文中报道的训练时间。通过定性仿真来计算定性的无碰撞轨迹，反应性地执行，同时动态地适应系统的数字特性。在V-REP机器人模拟器中进行并评估了实验。