Simultaneous State Estimation and Online Model Learning in a Soft Robotic System
🤖 AI Summary
This work addresses the challenge of simultaneously estimating states and learning dynamics for soft robots under noisy measurements. The authors propose an online learning framework that integrates marginalized particle filtering with Gaussian process regression. Leveraging a constant-curvature kinematic model and base reaction force observations, the method jointly performs pose estimation and nonparametric online identification of bending stiffness, eliminating the conventional random-walk assumption and thereby significantly enhancing model generalization. Experimental validation on a physical soft robotic platform demonstrates high-accuracy pose estimation and a substantial reduction in multi-step-ahead prediction errors, confirming the effectiveness and superiority of the learned dynamics model.
Application Category
📝 Abstract
Operating complex real-world systems, such as soft robots, can benefit from precise predictive control schemes that require accurate state and model knowledge. This knowledge is typically not available in practical settings and must be inferred from noisy measurements. In particular, it is challenging to simultaneously estimate unknown states and learn a model online from sequentially arriving measurements. In this paper, we show how a recently proposed gray-box system identification tool enables the estimation of a soft robot's current pose while at the same time learning a bending stiffness model. For estimation and learning, we rely solely on a nominal constant-curvature robot model and measurements of the robot's base reactions (e.g., base forces). The estimation scheme -- relying on a marginalized particle filter -- allows us to conveniently interface nominal constant-curvature equations with a Gaussian Process (GP) bending stiffness model to be learned. This, in contrast to estimation via a random walk over stiffness values, enables prediction of bending stiffness and improves overall model quality. We demonstrate, using real-world soft-robot data, that the method learns a bending stiffness model online while accurately estimating the robot's pose. Notably, reduced multi-step forward-prediction errors indicate that the learned bending-stiffness GP improves overall model quality.
Problem
Research questions and friction points this paper is trying to address.
state estimation
online model learning
soft robotics
system identification
bending stiffness
Innovation
Methods, ideas, or system contributions that make the work stand out.
simultaneous state estimation
online model learning
soft robotics
Gaussian Process
marginalized particle filter
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