This paper presents a novel simulation platform, ZeMa, designed for robotic manipulation tasks concerning soft objects. Such simulation ideally requires three properties: two-way soft-rigid coupling, intersection-free guarantees, and frictional contact modeling, with acceptable runtime suitable for deep and reinforcement learning tasks. Current simulators often satisfy only a subset of these needs, primarily focusing on distinct rigid-rigid or soft-soft interactions. The proposed ZeMa prioritizes physical accuracy and integrates the incremental potential contact method, offering unified dynamics simulation for both soft and rigid objects. It efficiently manages soft-rigid contact, operating 75x faster than baseline tools with similar methodologies like IPC-GraspSim. To demonstrate its applicability, we employ it for parallel grasp generation, penetrated grasp repair, and reinforcement learning for grasping, successfully transferring the trained RL policy to real-world scenarios.

翻译：本文提出一种新型仿真平台ZeMa，专为涉及软体物体的机器人操控任务设计。此类仿真在理论上需满足三项特性：双向软-刚耦合、无交叉保证、摩擦接触建模，且运行时间需满足深度学习和强化学习任务的需求。现有仿真器通常仅满足部分需求，主要侧重于刚-刚或软-软交互的区分处理。本研究提出的ZeMa平台优先保证物理精度，集成增量势接触方法，为软体和刚体物体提供统一动力学仿真。该平台高效管理软-刚接触，其运行速度比采用类似方法（如IPC-GraspSim）的基准工具快75倍。为验证其适用性，我们将其应用于并行抓取生成、穿透抓取修复及强化学习抓取任务，并成功将训练后的强化学习策略迁移至真实场景。