Soft, compliant robot hands passively adapt their shape to excel in grasping tasks. But their shape can also give insight into grasp quality, object properties and their environment. Recovering this shape is difficult because their soft material exhibits complex deformations when subjected to external force.
This thesis aims to enable limited shape sensing of the PneuFlex actuator used for the soft RBO Hand 2. Complex actuator shapes are identified as combinations of simple deformations that are shown to be predictable from strain sensor readings using machine learning. A layout of strain sensors is created from strain estimations during these deformations and optimized to produce a reduced layout of sensors.
This optimized layout is then shown to still accurately predict these deformations, giving insight into the shape of the PneuFlex actuator.