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This paper introduces RLFEM, a novel finite element method for simulating continuum robots that uses a reinforcement learning-enhanced quadratic programming solver to accelerate contact resolution. By integrating RL into a quasi-static FEM formulation, the method addresses the computational challenges of simulating contact-influenced behavior in these robots. Numerical experiments demonstrate a 16.20x speedup compared to a baseline FEM solver at a 150-node discretization level, without sacrificing accuracy.
Continuum robot simulations can now run 16x faster thanks to a reinforcement learning-boosted FEM solver that doesn't sacrifice accuracy.
Continuum robots exhibit exceptional flexibility and multi-degree-of-freedom maneuverability, offering significant advantages for navigating confined luminal spaces. However, rapid simulation of their contact-influenced behavior remains challenging. This paper presents RLFEM, an innovative finite element method (FEM) featuring a reinforcement learning-enhanced quadratic programming solver, specifically designed for efficient continuum robot simulation updates. To address complex robot dynamics, we employ a quasi-static FEM formulation. Our core contribution integrates an accelerated solver scheme within this framework, leveraging reinforcement learning to rapidly resolve FEM contact problems without accuracy compromise. RLFEM exhibits significant gains in runtime performance for continuum robot simulations, as evidenced by numerical experiments showing a 16.20-fold acceleration compared to the baseline at the 150-node discretization level.
