Compliance in soft hands can be both beneficial and detrimental to functionality. Although recent work has shown the benefits of compliance to object and environment geometry, there is little work in identifying and avoiding the negative aspects of compliance while controlling soft hands. However, a planner or a feedback-controller that exploits compliance should avoid the regions of detrimental morphological computation and guide the interactions to the favorable ones.
Luckily recent work in simulation has shown promising results in differentiating between beneficial/detrimental morphological computations. The challenge ahead is to whether these results can be transferred to real systems. Our lab's work in hand sensorization is a possible tool in this path.
In this thesis, the student will extend the analysis tools that were used for the grasping simulation data to real life scenarios. He/she will first test the analysis in simulation with different input modalities that can be generated in real life. If successful, direct application to the robot will be the next step. If not successful, the analysis will be altered to exploit the sensorization of the real hand.