A Synergistic Framework for Learning Shape Estimation and Shape-Aware Whole-Body Control Policy for Continuum Robots

TL;DR

This paper introduces a novel neural ODE-based framework that jointly estimates shape and controls continuum robots, improving accuracy and robustness over existing methods through simulation and real-world tests.

Contribution

It proposes a synergistic neural ODE framework combining shape estimation and control for continuum robots, integrating prior knowledge and enhancing nonlinear dynamics handling.

Findings

Outperforms state-of-the-art models in shape estimation accuracy.

Demonstrates robust trajectory tracking and obstacle avoidance.

Effective in both simulation and real-world environments.

Abstract

In this paper, we present a novel synergistic framework for learning shape estimation and a shape-aware whole-body control policy for tendon-driven continuum robots. Our approach leverages the interaction between two Augmented Neural Ordinary Differential Equations (ANODEs) -- the Shape-NODE and Control-NODE -- to achieve continuous shape estimation and shape-aware control. The Shape-NODE integrates prior knowledge from Cosserat rod theory, allowing it to adapt and account for model mismatches, while the Control-NODE uses this shape information to optimize a whole-body control policy, trained in a Model Predictive Control (MPC) fashion. This unified framework effectively overcomes limitations of existing data-driven methods, such as poor shape awareness and challenges in capturing complex nonlinear dynamics. Extensive evaluations in both simulation and real-world environments demonstrate the framework's robust performance in shape estimation, trajectory tracking, and obstacle avoidance. The proposed method consistently outperforms state-of-the-art end-to-end, Neural-ODE, and Recurrent Neural Network (RNN) models, particularly in terms of tracking accuracy and generalization capabilities.