# Complementarity-Free Multi-Contact Modeling and Optimization for Dexterous Manipulation

arXiv: https://arxiv.org/abs/2408.07855 · HTML: https://mlsanity.com/paper/2408.07855/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2024.

## Built on by (most cited first, 4 of 4)

- [DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References](https://mlsanity.com/paper/2502.09614/) (2025)
- [DexMan: Learning Bimanual Dexterous Manipulation from Human and Generated Videos](https://mlsanity.com/paper/2510.08475/) (2025)
- [UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations](https://mlsanity.com/paper/2512.11609/) (2025)
- [Physics-Informed Eikonal Caging for Whole-Arm Manipulation Planning](https://mlsanity.com/paper/2606.22143/) (2026)

## Abstract (arXiv)

A significant barrier preventing model-based methods from achieving real-time and versatile dexterous robotic manipulation is the inherent complexity of multi-contact dynamics. Traditionally formulated as complementarity models, multi-contact dynamics introduces non-smoothness and combinatorial complexity, complicating contact-rich planning and optimization. In this paper, we circumvent these challenges by introducing a lightweight yet capable multi-contact model. Our new model, derived from the duality of optimization-based contact models, dispenses with the complementarity constructs entirely, providing computational advantages such as closed-form time stepping, differentiability, automatic satisfaction with Coulomb friction law, and minimal hyperparameter tuning. We demonstrate the effectiveness and efficiency of the model for planning and control in a range of challenging dexterous manipulation tasks, including fingertip 3D in-air manipulation, TriFinger in-hand manipulation, and Allegro hand on-palm reorientation, all performed with diverse objects. Our method consistently achieves state-of-the-art results: (I) a 96.5% average success rate across all objects and tasks, (II) high manipulation accuracy with an average reorientation error of 11deg and position error of 7.8mm, and (III) contact-implicit model predictive control running at 50-100 Hz for all objects and tasks. These results are achieved with minimal hyperparameter tuning.