mlsanityData to 14 Oct 2026

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

arXiv 2509.00329 ↗First posted 30 Aug 2025cs.RO cs.AI cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 23 references that are in the map. First posted on arXiv 30 Aug 2025.

Builds on

8 of 23 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2015Continuous control with deep reinforcement learningcited by 3,407
  4. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 743
  5. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 465
  6. 2016Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updatescited by 353
  7. 2017Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuningcited by 297
  8. 2023Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environmentscited by 6

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  1. 2025Enhanced Robotic Navigation in Deformable Environments using Learning from Demonstration and Dynamic Modulation
  2. 20263D RL-DWA: A Hybrid Reinforcement Learning and Dynamic Window Approach for Goal-Directed Local Navigation in Multi-DoF Robots
  3. 2024Learning-based Control for Tendon-Driven Continuum Robotic Arms
  4. 2026Self-Curriculum Model-based Reinforcement Learning for Shape Control of Deformable Linear Objects
  5. 2026Physics-Informed Policy Optimization via Analytic Dynamics Regularization
  6. 2026From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution
  7. 2026Actuator Dynamics Curricula for Narrow-Viability Tasks in Legged Robot Learning
  8. 2026History-Conditioned Flow Matching for Probabilistic Dynamics of Tendon-Driven Continuum Robots