# Contact-Implicit Model Predictive Control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach

arXiv: https://arxiv.org/abs/2402.18897 · HTML: https://mlsanity.com/paper/2402.18897/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [Diffusing Trajectory Optimization Problems for Recovery During Multi-Finger Manipulation](https://mlsanity.com/paper/2510.07030/) (2025)
- [Balance Equation-based Distributionally Robust Offline Imitation Learning](https://mlsanity.com/paper/2511.07942/) (2025)
- [UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations](https://mlsanity.com/paper/2512.11609/) (2025)
- [Tele-Catch: Adaptive Teleoperation for Dexterous Dynamic 3D Object Catching](https://mlsanity.com/paper/2603.28427/) (2026)
- [Amortising Trajectory Optimisation for Residual MPC via Implicit Contact Differentiation](https://mlsanity.com/paper/2607.24959/) (2026)
- [EgoPHI: Estimating 3D Hand-Object Contact and Force from Egocentric Vision](https://mlsanity.com/paper/2608.13014/) (2026)
- [StableGrasp: Reconstructing Physically Stable Human Hand Grasps from Single Images](https://mlsanity.com/paper/2610.09195/) (2026)

## Abstract (arXiv)

Dexterous in-hand manipulation is an essential skill of production and life. However, the highly stiff and mutable nature of contacts limits real-time contact detection and inference, degrading the performance of model-based methods. Inspired by recent advances in contact-rich locomotion and manipulation, this paper proposes a novel model-based approach to control dexterous in-hand manipulation and overcome the current limitations. The proposed approach has an attractive feature, which allows the robot to robustly perform long-horizon in-hand manipulation without predefined contact sequences or separate planning procedures. Specifically, we design a high-level contact-implicit model predictive controller to generate real-time contact plans executed by the low-level tracking controller. Compared to other model-based methods, such a long-horizon feature enables replanning and robust execution of contact-rich motions to achieve large displacements in-hand manipulation more efficiently; Compared to existing learning-based methods, the proposed approach achieves dexterity and also generalizes to different objects without any pre-training. Detailed simulations and ablation studies demonstrate the efficiency and effectiveness of our method. It runs at 20Hz on the 23-degree-of-freedom, long-horizon, in-hand object rotation task.