实时安全操控绳布，用并行仿真与鲁棒控制实现毫秒级规划
Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation
- 基于GPU并行可微仿真与接触平滑，支持间歇接触下的梯度规划
- 毫秒级规划速度，任务成功率、安全性和速度均优于基线
- 适合需要高可靠性的柔性物体操控场景，如机器人折叠、穿绳
我们提出CORD-SLS，一种面向绳索和布料等柔性物体的实时安全操控方法。核心是基于GPU并行的可微分仿真器，通过接触平滑实现间歇接触下的高效梯度规划。为应对模型与感知不确定性，设计了实时、GPU并行的输出反馈鲁棒模型预测控制（MPC）算法，结合该仿真器进行规划。进一步利用置信预测校准视觉反馈与感知误差边界，生成可达管，实现高概率安全控制。在仿真与真实硬件上评估了复杂接触任务，包括避障、穿引、折叠与展平。实验表明，CORD-SLS在各类场景中均实现毫秒级规划，且在安全性、速度与任务成功率上超越现有方法。
原文摘要 · Abstract (English)
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.
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