Soft robots have desirable qualities for use in underwater environments thanks to their inherent compliance and lack of need for exposed hardware. Nevertheless, these advantages come at the cost of considerable control challenges. Data-driven model predictive control (MPC) is an approach that has shown promise in controlling soft robots. However, manually tuning the many hyperparameters in the learned dynamics model and the optimizer can be extremely tedious. In this work, we explore using data-driven MPC to control an underwater soft robot, and employ the AutoMPC method to automatically tune the hyperparameters and generate the controller. In the process, we extend AutoMPC's capabilities to handle multi-task tuning and we add a barrier cost function to enforce actuator constraints. Our experiments show that the AutoMPC controller reaches targets with significantly higher accuracy and reliability than state-of-the-art baselines both in- and out-of-distribution of the training data. 

Automatically-Tuned Model Predictive Control for an Underwater Soft Robot

Model predictive control (MPC) has been applied to many platforms in robotics and autonomous systems for its capability to predict a system's future behavior while incorporating constraints that a system may have. To enhance the performance of a system with an MPC controller, one can manually tune the MPC's cost function. However, it can be challenging due to the possibly high dimension of the parameter space as well as the potential difference between the open-loop cost function in MPC and the overall closed-loop performance metric function. This paper presents DiffTune-MPC, a novel learning method, to learn the cost function of an MPC in a closed-loop manner. The proposed framework is compatible with the scenario where the time interval for performance evaluation and MPC's planning horizon have different lengths. We show the auxiliary problem whose solution admits the analytical gradients of MPC and discuss its variations in different MPC settings. Simulation results demonstrate the capability of DiffTune-MPC and illustrate the influence of constraints (from actuation limits) on learning.

DiffTune-MPC: Closed-Loop Learning for Model Predictive Control

Applications of metaheuristic optimization algorithms in model predictive control for chemical engineering processes: A systematic review

Soft robots grew to prominence in large part because they promised a new and exciting means for engineers to develop robots that are both highly adaptable and safe for direct human interaction. However, despite showing substantial promise in this area, soft robots have not yet seen widespread adoption. Two major factors that have prevented the development of soft robots are the fundamental challenges of both the modeling and control of soft structures. While traditional robots enjoy a myriad of theoretical and computational tools for modeling and control, the options for soft robots are far more limited. In this work, we introduce a physics-based finite element simulation platform, Kraken, that can be used to accurately model the dynamic and oscillatory motions of soft robots. After a brief theoretical introduction to hyperelastic modeling and the finite element method, we show the utility of our approach by simulating the oscillations of a 1D hyperelastic actuator, the dynamics of a 2D hyperelastic pendulum, and a 3D spherical hyperelastic pendulum. We then demonstrate the accuracy of our approach by presenting the agreement of the simulated results with those obtained via physical experiments for three materials with different hyperelastic properties, with percent errors as low as 1%. Taken together, these results demonstrate that the aforementioned simulation platform is a critical step towards the fast and accurate simulation, prototyping, and control of soft robots.

Physics-Based Finite Element Simulation of the Dynamics of Soft Robots

AutoMPC is a Python package that automates and optimizes data-driven model predictive control. However, it can be computationally expensive and unstable when exploring large search spaces using pure Bayesian Optimization (BO). To address these issues, this paper proposes to employ a meta-learning approach called Portfolio that improves AutoMPC's efficiency and stability by warmstarting BO. Portfolio optimizes initial designs for BO using a diverse set of configurations from previous tasks and stabilizes the tuning process by fixing initial configurations instead of selecting them randomly. Experimental results demonstrate that Portfolio outperforms the pure BO in finding desirable solutions for AutoMPC within limited computational resources on 11 nonlinear control simulation benchmarks and 1 physical underwater soft robot dataset.

Efficient Automatic Tuning for Data-driven Model Predictive Control via Meta-Learning

Many soft-body organisms found in nature flourish underwater. Similarly, soft robots are potentially well-suited for underwater environments partly because the problematic effects of gravity, friction, and harmonic oscillations are less severe underwater. However, it remains a challenge to design, fabricate, waterproof, model, and control underwater soft robotic systems. Furthermore, submersible robots usually do not have configurable components because of the need for sealed electronics and mechanical elements. This work presents the development of a modular and submersible soft robotic arm driven by hydraulic actuators which consists of mostly 3D printable parts and can be assembled or modified in a relatively short amount of time. Its modular design enables multiple shape configurations and easy swapping of soft actuators. As a first step to exploring machine learning control algorithms on this system, we also present preliminary forward and inverse kinematics models developed using deep neural networks.

Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models

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Automatically-Tuned Model Predictive Control for an Underwater Soft Robot

Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models