Physics simulation for contact-rich robotics is often bottlenecked by contact resolution: mainstream engines enforce non-penetration and Coulomb friction via complementarity constraints or constrained optimization, requiring per-step iterative solves whose cost grows superlinearly with contact density. We present ComFree-Sim, a GPU-parallelized analytical contact physics engine built on complementarity-free contact modeling. ComFree-Sim computes contact impulses in closed form via an impedance-style prediction--correction update in the dual cone of Coulomb friction. Contact computation decouples across contact pairs and becomes separable across cone facets, mapping naturally to GPU kernels and yielding near-linear runtime scaling with the number of contacts. We further extend the formulation to a unified 6D contact model capturing tangential, torsional, and rolling friction, and introduce a practical dual-cone impedance heuristic. ComFree-Sim is implemented in Warp and exposed through a MuJoCo-compatible interface as a drop-in backend alternative to MuJoCo Warp (MJWarp). Experiments benchmark penetration, friction behaviors, stability, and simulation runtime scaling against MJWarp, demonstrating near-linear scaling and 2--3 times higher throughput in dense contact scenes with comparable physical fidelity. We deploy ComFree-Sim in real-time MPC for in-hand dexterous manipulation on a real-world multi-fingered LEAP hand and in dynamics-aware motion retargeting, demonstrating that low-latency simulation yields higher closed-loop success rates and enables practical high-frequency control in contact-rich tasks.

翻译：接触密集型机器人的物理仿真通常受限于接触解析：主流引擎通过互补性约束或约束优化来强制执行无穿透和库仑摩擦，这需要每步进行迭代求解，其计算成本随接触密度超线性增长。我们提出了ComFree-Sim，一个基于无互补性接触建模的GPU并行化解析接触物理引擎。ComFree-Sim通过库仑摩擦对偶锥中的阻抗式预测-校正更新，以闭式形式计算接触冲量。接触计算在接触对之间解耦，并可在锥体各面上分离，自然地映射到GPU内核，使得运行时随接触数量呈近线性扩展。我们进一步将该公式扩展为统一的6D接触模型，以捕捉切向、扭转和滚动摩擦，并引入了一种实用的对偶锥阻抗启发式方法。ComFree-Sim在Warp中实现，并通过一个与MuJoCo兼容的接口作为MuJoCo Warp（MJWarp）的即插即用后端替代方案提供。实验针对穿透、摩擦行为、稳定性和仿真运行时扩展性对MJWarp进行了基准测试，结果表明在物理保真度相当的情况下，在密集接触场景中实现了近线性扩展和2-3倍的吞吐量提升。我们将ComFree-Sim部署于真实世界多指LEAP手上的灵巧手内操作的实时模型预测控制中，以及动态感知的运动重定向中，证明了低延迟仿真能带来更高的闭环成功率，并能在接触密集型任务中实现实用的高频控制。