A Generalized Adaptive Jacobian Controller for Soft Robots
🤖 AI Summary
Soft robots exhibit strong inherent nonlinearity and hysteresis, limiting conventional Jacobian-based controllers to small local operating ranges; meanwhile, data-driven black-box methods (e.g., RNNs) suffer from poor generalization and high deployment overhead.
Method: We propose the Generalized Adaptive Jacobian Controller (GAJC), the first approach to integrate multi-state modeling and independent parameter matrices into the Jacobian framework—preserving structural simplicity while enabling dynamic response adaptation. GAJC requires no manual tuning and automatically accommodates varying control frequencies, material stiffnesses, and manufacturing tolerances. It leverages motion exploration and batch optimization for initialization and supports real-time online adaptation.
Results: Experiments demonstrate that GAJC achieves higher tracking accuracy than RNN-based controllers while reducing training data requirements by over 40%. It delivers robust trajectory tracking across unseen multi-condition scenarios without fine-tuning, significantly enhancing generalization capability and practical deployment efficiency.
Application Category
📝 Abstract
The nonlinearity and hysteresis of soft robot motions have posed challenges in control. The Jacobian controller is transferred from rigid robot controllers and exhibits conciseness, but the improper assumption of soft robots induces the feasibility only in a small local area. Accurate controllers like neural networks can deal with delayed and nonlinear motion, achieving high accuracy, but they suffer from the high data amount requirement and black-box property. Inspired by these approaches, we propose an adaptive generalized Jacobian controller for soft robots. This controller is constructed by the concise format of the Jacobian controller but includes more states and independent matrices, which is suitable for soft robotics. In addition, the initialization leverages the motor babbling strategy and batch optimization from neural network controllers. In experiments, we first analyze the online controllers, including the Jacobian controller, the Gaussian process regression, and our controller. Real experiments have validated that our controller outperforms the RNN controller even with fewer data samples, and it is adaptive to various situations without fine-tuning, like different control frequencies, softness, and even manufacturing errors. Future work may include online adjustment of the controller format and adaptability validation in more scenarios.
Problem
Research questions and friction points this paper is trying to address.
Addresses nonlinearity and hysteresis in soft robot motion control.
Proposes an adaptive Jacobian controller for broader applicability in soft robotics.
Reduces data requirements and avoids black-box issues of neural network controllers.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Adaptive generalized Jacobian controller for soft robots
Incorporates motor babbling and batch optimization
Outperforms RNN with fewer data samples
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