Learning Reduced-Order Soft Robot Controller

Deformable robots are notoriously difficult to model or control due to its high-dimensional configuration spaces. Direct trajectory optimization suffers from the curse-of-dimensionality and incurs a high computational cost, while learning-based controller optimization methods are sensitive to hyper-parameter tuning. To overcome these limitations, we hypothesize that high fidelity soft robots can be both simulated and controlled by restricting to low-dimensional spaces. Under such assumption, we propose a two-stage algorithm to identify such simulation- and control-spaces. Our method first identifies the so-called simulation-space that captures the salient deformation modes, to which the robot's governing equation is restricted. We then identify the control-space, to which control signals are restricted. We propose a multi-fidelity Riemannian Bayesian bilevel optimization to identify task-specific control spaces. We show that the dimension of control-space can be less than 10 for a high-DOF soft robot to accomplish walking and swimming tasks, allowing low-dimensional MPC controllers to be applied to soft robots with tractable computational complexity.

Authors

• Chen Liang
• Xifeng Gao
• Kui Wu 0003
• Zherong Pan

Keywords

• Computational modeling
• Soft robotics
• Aerospace electronics
• Robot sensing systems
• Bayes methods
• Task analysis
• Read only memory
• Robot Control
• Soft Robots
• Computational Cost
• Control Signal
• Model Predictive Control
• Configuration Space
• Trajectory Optimization
• Deformation Modes
• Bayesian Optimization
• Walking Task
• Bilevel Optimization
• Degrees Of Freedom
• System Dynamics
• Dynamic Model
• Actuator
• External Force
• Control Design
• Finite Element Method
• Kernel Function
• Gaussian Process
• Reduced-order Model
• Locomotion Tasks
• Reward Function
• Gauss-Newton Method
• Control Scheme Design
• Walking Direction
• Geodesic Distance
• Low-dimensional Subspace
• Simulated Robot
• Level Of Fidelity