# BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning

arXiv: https://arxiv.org/abs/2201.07601 · HTML: https://mlsanity.com/paper/2201.07601/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2022.

## Built on by (most cited first, 8 of 8)

- [Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control](https://mlsanity.com/paper/2401.16889/) (2024)
- [What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?](https://mlsanity.com/paper/2512.24497/) (2025)
- [A Survey of Optimization-based Task and Motion Planning: From Classical To Learning Approaches](https://mlsanity.com/paper/2404.02817/) (2024)
- [A Unified and General Humanoid Whole-Body Controller for Versatile Locomotion](https://mlsanity.com/paper/2502.03206/) (2025)
- [Humanoid Whole-Body Locomotion on Narrow Terrain via Dynamic Balance and Reinforcement Learning](https://mlsanity.com/paper/2502.17219/) (2025)
- [MARG: MAstering Risky Gap Terrains for Legged Robots with Elevation Mapping](https://mlsanity.com/paper/2509.20036/) (2025)
- [Ask1: Development and Reinforcement Learning-Based Control of a Custom Quadruped Robot](https://mlsanity.com/paper/2412.08019/) (2024)
- [Learning Natural and Robust Hexapod Locomotion over Complex Terrains via Motion Priors based on Deep Reinforcement Learning](https://mlsanity.com/paper/2511.03167/) (2025)

## Abstract (arXiv)

Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear MPC framework, the BiConMP which can generate whole body trajectories online by efficiently exploiting the structure of the robot dynamics. BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes and transitioning online between different gaits. Further, the ability of BiConMP to generate non-trivial acyclic whole-body dynamic motions on the robot is presented. The same approach is also used to generate various dynamic motions in MPC on a humanoid robot (Talos) and another quadruped robot (AnYmal) in simulation. Finally, an extensive empirical analysis on the effects of planning horizon and frequency on the nonlinear MPC framework is reported and discussed.