# Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models

arXiv: https://arxiv.org/abs/2206.10787 · HTML: https://mlsanity.com/paper/2206.10787/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2022.

## Built on by (most cited first, 30 of 49)

- [Visual Dexterity: In-Hand Reorientation of Novel and Complex Object Shapes](https://mlsanity.com/paper/2211.11744/) (2022)
- [3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing](https://mlsanity.com/paper/2410.24091/) (2024)
- [ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via Residual Learning](https://mlsanity.com/paper/2503.21860/) (2025)
- [AdaptiGraph: Material-Adaptive Graph-Based Neural Dynamics for Robotic Manipulation](https://mlsanity.com/paper/2407.07889/) (2024)
- [Compositional Diffusion-Based Continuous Constraint Solvers](https://mlsanity.com/paper/2309.00966/) (2023)
- [DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation](https://mlsanity.com/paper/2505.24853/) (2025)
- [RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.04150/) (2023)
- [Dynamic-Resolution Model Learning for Object Pile Manipulation](https://mlsanity.com/paper/2306.16700/) (2023)
- [AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch](https://mlsanity.com/paper/2405.07391/) (2024)
- [Physics-Driven Data Generation for Contact-Rich Manipulation via Trajectory Optimization](https://mlsanity.com/paper/2502.20382/) (2025)
- [Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching](https://mlsanity.com/paper/2306.14079/) (2023)
- [Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness](https://mlsanity.com/paper/2403.10454/) (2024)
- [DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References](https://mlsanity.com/paper/2502.09614/) (2025)
- [Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models](https://mlsanity.com/paper/2410.09163/) (2024)
- [Touch begins where vision ends: Generalizable policies for contact-rich manipulation](https://mlsanity.com/paper/2506.13762/) (2025)
- [QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer](https://mlsanity.com/paper/2404.07988/) (2024)
- [DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos](https://mlsanity.com/paper/2510.08475/) (2025)
- [Smoothed Online Learning for Prediction in Piecewise Affine Systems](https://mlsanity.com/paper/2301.11187/) (2023)
- [Learning Reusable Manipulation Strategies](https://mlsanity.com/paper/2311.03293/) (2023)
- [Vegetable Peeling: A Case Study in Constrained Dexterous Manipulation](https://mlsanity.com/paper/2407.07884/) (2024)
- [DexNDM: Closing the Reality Gap for Dexterous In-Hand Rotation via Joint-Wise Neural Dynamics Model](https://mlsanity.com/paper/2510.08556/) (2025)
- [Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation](https://mlsanity.com/paper/2408.01258/) (2024)
- [One-Shot Manipulation Strategy Learning by Making Contact Analogies](https://mlsanity.com/paper/2411.09627/) (2024)
- [Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation](https://mlsanity.com/paper/2412.02676/) (2024)
- [FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems](https://mlsanity.com/paper/2604.28156/) (2026)
- [Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration](https://mlsanity.com/paper/2607.00033/) (2026)
- [Differentiable Simulation of Hard Contacts with Soft Gradients for Learning and Control](https://mlsanity.com/paper/2506.14186/) (2025)
- [Diffusion-Informed Probabilistic Contact Search for Multi-Finger Manipulation](https://mlsanity.com/paper/2410.00841/) (2024)
- [Diffusion-Based Impedance Learning for Contact-Rich Manipulation Tasks](https://mlsanity.com/paper/2509.19696/) (2025)
- [DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation](https://mlsanity.com/paper/2411.02189/) (2024)

## Abstract (arXiv)

The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficulties are often attributed to (i) the explosion of contact modes, (ii) stiff, non-smooth contact dynamics and the resulting exploding / discontinuous gradients, and (iii) the non-convexity of the planning problem. The stochastic nature of RL addresses (i) and (ii) by effectively sampling and averaging the contact modes. On the other hand, model-based methods have tackled the same challenges by smoothing contact dynamics analytically. Our first contribution is to establish the theoretical equivalence of the two methods for simple systems, and provide qualitative and empirical equivalence on a number of complex examples. In order to further alleviate (ii), our second contribution is a convex, differentiable and quasi-dynamic formulation of contact dynamics, which is amenable to both smoothing schemes, and has proven through experiments to be highly effective for contact-rich planning. Our final contribution resolves (iii), where we show that classical sampling-based motion planning algorithms can be effective in global planning when contact modes are abstracted via smoothing. Applying our method on a collection of challenging contact-rich manipulation tasks, we demonstrate that efficient model-based motion planning can achieve results comparable to RL with dramatically less computation. Video: https://youtu.be/12Ew4xC-VwA