CRESSim-Neo: 手術ロボティクスとロボット学習のためのバッチ処理GPUシミュレーションエンジン
CRESSim-Neo: A Batched GPU Simulation Engine for Surgical Robotics and Robot Learning
手術ロボティクスとロボット学習のための、剛体・変形組織・流体・紐などを扱えるバッチ処理GPUシミュレーションエンジンを開発した。PyTorchと直接連携でき、高速な並列シミュレーションを実現する。
著者: Yafei Ou, Ahnaf Naheen, Tleukhan Mussin, Hans Jarales, Melwin Moncy, Mahdi Tavakoli
分類: cs.RO
原文アブストラクト
We introduce CRESSim-Neo, a batched GPU simulation engine for surgical robotics and robot learning. CRESSim-Neo combines position-based simulation of rigid bodies, deformable tissues, fluids, and strands with batched rendering, surgery-specific sensing, and a GPU-resident data pipeline. The engine supports applications including tissue manipulation, fluid suction, suturing, cable-driven robots, and ultrasound image synthesis. Direct access to physics and rendering buffers enables GPU-resident robot learning and zero-copy PyTorch integration using DLPack. We demonstrate CRESSim-Neo across rigid-body, deformable-body, and fluid simulation tasks, including vision-based and surgical robot-learning scenarios. On an NVIDIA RTX 4090, the engine achieves up to 2.03 million environment steps per second for 8192 parallel CartPole environments, and scales to batched surgical scenarios involving tissue deformation, fluid interaction, and ultrasound sensing. Overall, CRESSim-Neo provides a unified and scalable platform for surgical simulation, synthetic data generation, and surgical robot learning.