  Soft robots are distinguished by their flexible and adaptable, allowing them
to perform tasks that are nearly impossible for rigid robots. However,
controlling their configuration is challenging due to their nonlinear material
response and infinite deflection degrees of freedom. A potential solution is to
discretize the infinite-dimensional configuration space of soft robots into a
finite but sufficiently large number of functional shapes. This study explores
a co-design strategy for pneumatically actuated soft grippers with multiple
encoded stable states, enabling desired functional shape and stiffness
reconfiguration. An energy based analytical model for soft multistable grippers
is presented, mapping the robots' infinite-dimensional configuration space into
discrete stable states, allowing for prediction of the systems final state and
dynamic behavior. Our approach introduces a general method to capture the soft
robots' response with the lattice lumped parameters using automatic relevance
determination regression, facilitating inverse co-design. The resulting
computationally efficient model enables us to explore the configuration space
in a tractable manner, allowing the inverse co-design of our robots by setting
desired targeted positions with optimized stiffness of the set targets. This
strategy offers a framework for controlling soft robots by exploiting the
nonlinear mechanics of multistable structures, thus embodying mechanical
intelligence into soft structures.
