Robustness without Wrinkles: Parallel Simulation & Robust MPC for Certified Deformable Manipulation

Wei-Chen Li^*, Jeffrey Fang^*, Sasanka Polisetti, Yuexi Song, Glen Chou

Georgia Institute of Technology
^*Indicates Equal Contribution

Leveraging GPU-accelerated contact-smoothed differentiable simulation and output-feedback system level synthesis, we enable real-time manipulation of deformable objects involving intermittent contact while remaining robust to dynamics and visual uncertainty.

Abstract

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.

Benchmark Tasks

Lift rope

Drag rope

Fold cloth

Flatten cloth

BibTeX

@article{li2026robustness,
  title={Robustness without Wrinkles: Parallel Simulation & Robust MPC for Certified Deformable Manipulation},
  author={Wei-Chen Li and Jeffrey Fang and Sasanka Polisetti and Yuexi Song and Glen Chou},
  year={2026},
}