The Importance and the Limitations of Sim2Real for Robotic Manipulation in Precision Agriculture
This highlights a critical bottleneck for precision agriculture robotics, though it is an incremental analysis of existing challenges.
The paper examines the limitations of Sim2Real techniques in agricultural robotics, where current simulation software lacks the accuracy needed for tasks requiring detailed dynamics and visuals, despite these methods helping mitigate the simulation-reality gap.
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.