Jacobian Exploratory Dual-Phase Reinforcement Learning for Dynamic Endoluminal Navigation of Deformable Continuum Robots

• URL: http://arxiv.org/abs/2509.00329v1
• Date: Sat, 30 Aug 2025 03:04:35 GMT
• Title: Jacobian Exploratory Dual-Phase Reinforcement Learning for Dynamic Endoluminal Navigation of Deformable Continuum Robots
• Authors: Yu Tian, Chi Kit Ng, Hongliang Ren,
• Abstract summary: Deformable continuum robots (DCRs) present unique planning challenges due to nonlinear deformation mechanics and partial state observability.<n>This paper proposes Jacobian Exploratory Dual-Phase RL (JEDP-RL), a framework that decomposes planning into phased Jacobian estimation and policy execution.
• Score: 11.169457209940857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformable continuum robots (DCRs) present unique planning challenges due to nonlinear deformation mechanics and partial state observability, violating the Markov assumptions of conventional reinforcement learning (RL) methods. While Jacobian-based approaches offer theoretical foundations for rigid manipulators, their direct application to DCRs remains limited by time-varying kinematics and underactuated deformation dynamics. This paper proposes Jacobian Exploratory Dual-Phase RL (JEDP-RL), a framework that decomposes planning into phased Jacobian estimation and policy execution. During each training step, we first perform small-scale local exploratory actions to estimate the deformation Jacobian matrix, then augment the state representation with Jacobian features to restore approximate Markovianity. Extensive SOFA surgical dynamic simulations demonstrate JEDP-RL's three key advantages over proximal policy optimization (PPO) baselines: 1) Convergence speed: 3.2x faster policy convergence, 2) Navigation efficiency: requires 25% fewer steps to reach the target, and 3) Generalization ability: achieve 92% success rate under material property variations and achieve 83% (33% higher than PPO) success rate in the unseen tissue environment.

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