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

2025-01-07

AI Summary

To address the challenge of tightly coupling real-time shape sensing and whole-body control for tendon-driven continuum robots under dynamic deformation, this paper proposes a synergistic learning framework based on dual-coupled Augmented Neural ODEs—Shape-NODE and Control-NODE. For the first time, Cosserat rod theory is embedded as a physics-informed prior into neural differential equations, enabling joint optimization of physically grounded real-time shape modeling and model-predictive-control (MPC)-style closed-loop control. The framework is trained end-to-end via differentiable simulation, unifying shape estimation and motion control objectives. Experiments on both simulation and physical platforms demonstrate a 37% reduction in shape estimation error, a 24% improvement in trajectory tracking success rate, and significantly enhanced generalization capability for obstacle avoidance—outperforming baseline methods including standard Neural-ODEs, RNNs, and end-to-end learning approaches.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Soft Robotic
• Shape Perception
• Control Complexity

Innovation

• Soft Robotic Control
• Complementary Intelligent Equations
• Advanced Rod Theory