We present CORD-SLS, a real-time control method for safe deformable object manipulation, with a focus on ropes and cloth. At its core is a GPU-parallel differentiable simulator with contact smoothing which enables efficient gradient-based planning through intermittent contact. To robustly satisfy constraints under model and sensing uncertainty, we develop a real-time, GPU-parallel output-feedback robust model predictive control (MPC) algorithm that plans with this simulator. We further show that the simulator accelerates model-based RL for training neural manipulation policies. To improve real-world robustness, we use conformal prediction to calibrate visual-feedback and perception-error bounds for MPC, producing reachable tubes that enable high-probability safe control. We evaluate CORD-SLS on high-dimensional, contact-rich rope and cloth manipulation tasks in simulation and hardware, including obstacle avoidance, routing, folding, and smoothing. Across settings, CORD-SLS achieves millisecond-speed planning, exceeding baselines in safety, speed, and task success.

翻译：本文提出CORD-SLS，一种面向绳索和布料等可形变物体安全操控的实时控制方法。其核心是一个具有接触平滑功能的GPU并行可微仿真器，该仿真器能够通过间歇性接触实现高效的基于梯度的规划。为在模型与感知不确定性下鲁棒地满足约束条件，我们开发了一种基于该仿真器进行规划的实时GPU并行输出反馈鲁棒模型预测控制算法。进一步证明，该仿真器能加速基于模型的强化学习，从而训练神经操控策略。为提升实际应用中的鲁棒性，我们采用保形预测方法校准MPC的视觉反馈与感知误差边界，生成可达管道以实现高概率安全控制。我们在仿真与硬件平台上对CORD-SLS进行了高维、强接触的绳索和布料操控任务评估，包括避障、布线、折叠和平整等操作。实验表明，CORD-SLS在多种场景下均能实现毫秒级规划速度，在安全性、速度及任务成功率方面均超越基线方法。