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

arXiv: https://arxiv.org/abs/2606.14188 · HTML: https://mlsanity.com/paper/2606.14188/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 60 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 30 of 38)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Do Differentiable Simulators Give Better Policy Gradients?](https://mlsanity.com/paper/2202.00817/) (2022)
- [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020)
- [Learning Predictive Representations for Deformable Objects Using Contrastive Estimation](https://mlsanity.com/paper/2003.05436/) (2020)
- [DiffCloth: Differentiable Cloth Simulation with Dry Frictional Contact](https://mlsanity.com/paper/2106.05306/) (2021)
- [Latent Space Roadmap for Visual Action Planning of Deformable and Rigid Object Manipulation](https://mlsanity.com/paper/2003.08974/) (2020)
- [Bundled Gradients through Contact via Randomized Smoothing](https://mlsanity.com/paper/2109.05143/) (2021)
- [ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation](https://mlsanity.com/paper/2203.06856/) (2022)
- [DaXBench: Benchmarking Deformable Object Manipulation with Differentiable Physics](https://mlsanity.com/paper/2210.13066/) (2022)
- [Safe Output Feedback Motion Planning from Images via Learned Perception Modules and Contraction Theory](https://mlsanity.com/paper/2206.06553/) (2022)
- [Learning Deformable Object Manipulation from Expert Demonstrations](https://mlsanity.com/paper/2207.10148/) (2022)
- [Learning Latent Graph Dynamics for Visual Manipulation of Deformable Objects](https://mlsanity.com/paper/2104.12149/) (2021)
- [Benchmarking the Sim-to-Real Gap in Cloth Manipulation](https://mlsanity.com/paper/2310.09543/) (2023)
- [Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction](https://mlsanity.com/paper/2304.00194/) (2023)
- [Differentiable Particles for General-Purpose Deformable Object Manipulation](https://mlsanity.com/paper/2405.01044/) (2024)
- [Planning with Spatial-Temporal Abstraction from Point Clouds for Deformable Object Manipulation](https://mlsanity.com/paper/2210.15751/) (2022)
- [DeformNet: Latent Space Modeling and Dynamics Prediction for Deformable Object Manipulation](https://mlsanity.com/paper/2402.07648/) (2024)
- [Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects](https://mlsanity.com/paper/2406.05931/) (2024)
- [Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control](https://mlsanity.com/paper/2001.09950/) (2020)
- [Robust Nonlinear Optimal Control via System Level Synthesis](https://mlsanity.com/paper/2301.04943/) (2023)
- [Synthesizing Stable Reduced-Order Visuomotor Policies for Nonlinear Systems via Sums-of-Squares Optimization](https://mlsanity.com/paper/2304.12405/) (2023)
- [Safety Beyond the Training Data: Robust Out-of-Distribution MPC via Conformalized System Level Synthesis](https://mlsanity.com/paper/2602.12047/) (2026)
- [Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU](https://mlsanity.com/paper/2604.07644/) (2026)
- [TAPNext++: What's Next for Tracking Any Point (TAP)?](https://mlsanity.com/paper/2604.10582/) (2026)
- [Robustly Constrained Dynamic Games for Uncertain Nonlinear Dynamics](https://mlsanity.com/paper/2509.16826/) (2025)
- [D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation](https://mlsanity.com/paper/2403.12861/) (2024)
- [Planning and Control for Deformable Linear Object Manipulation](https://mlsanity.com/paper/2503.04007/) (2025)
- [Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning](https://mlsanity.com/paper/2502.11375/) (2025)
- [Guaranteed Robust Nonlinear MPC via Disturbance Feedback](https://mlsanity.com/paper/2509.18760/) (2025)

## Built on by (most cited first, 3 of 3)

- [Certified World Models as Sensing Clocks: Drift-Aware Deadlines for Active Perception](https://mlsanity.com/paper/2607.01537/) (2026)
- [GPU-Parallel Linearization Error Bounds for Real-Time Robust Optimal Control of Nonlinear and Neural Network Dynamics](https://mlsanity.com/paper/2607.01203/) (2026)
- [DreamLedger: Where to Refuse World-Model Imagination Using Execution-Settled Credit](https://mlsanity.com/paper/2608.23863/) (2026)

## Abstract (arXiv)

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.