论文标题
SIM2REAL对精确农业中机器人操作的重要性和局限性
The Importance and the Limitations of Sim2Real for Robotic Manipulation in Precision Agriculture
论文作者
论文摘要
近年来，SIM2REAL方法为机器人技术带来了很好的结果。基于模型的学习或域随机化等技术可以帮助克服模拟和现实之间的差距，但是在某些情况下，仍然需要模拟精度。一个例子是农业机器人技术，在动态和视觉效果方面需要详细的模拟。但是，仿真软件仍然无法具有这种质量和准确性。当前的SIM2REAL技术有助于缓解问题，但是对于这些特定任务，它们还不够。
In recent years Sim2Real approaches have brought great results to robotics. Techniques such as model-based learning or domain randomization can help overcome the gap between simulation and reality, but in some situations simulation accuracy is still needed. An example is agricultural robotics, which needs detailed simulations, both in terms of dynamics and visuals. However, simulation software is still not capable of such quality and accuracy. Current Sim2Real techniques are helpful in mitigating the problem, but for these specific tasks they are not enough.
