Soft robots can be constructed with few parts and from a wide variety of materials. This makes them a potentially appealing choice for applications where there are resource constraints on system fabrication. However, soft robot dynamics are difficult to accurately model analytically, due to a multiphysics coupling between shape, forces, temperature, and history of motion. Data-driven methods have successfully captured unmodeled behavior of soft systems on short timescales, but begin to fail during lifetime operation due to nonstationarity: changes in the dynamics over time. In this work, we propose a framework for model-based planning/control of soft robots with nonstationary dynamics and present a compliant parallel robot implementation that can be used to evaluate our proposed methods. We compare the potential behavior of analytical parameter estimation and instance-based learning in adaptation and propose a hybrid semi-parametric model for adaptation.
Carmel Majidi (Advisor)
Zoom Participation. See announcement.