# CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

arXiv: https://arxiv.org/abs/2405.11773 · HTML: https://mlsanity.com/paper/2405.11773/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2024.

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

- [BarlowWalk: Self-supervised Representation Learning for Legged Robot Terrain-adaptive Locomotion](https://mlsanity.com/paper/2508.00939/) (2025)
- [ECO: Energy-Constrained Optimization with Reinforcement Learning for Humanoid Walking](https://mlsanity.com/paper/2602.06445/) (2026)
- [Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair Climbing](https://mlsanity.com/paper/2411.01919/) (2024)
- [SRL: Combining SLIP Model and Reinforcement Learning for Agile Robotic Jumping](https://mlsanity.com/paper/2606.18625/) (2026)

## Abstract (arXiv)

Performing acrobatic maneuvers like dynamic jumping in bipedal robots presents significant challenges in terms of actuation, motion planning, and control. Traditional approaches to these tasks often simplify dynamics to enhance computational efficiency, potentially overlooking critical factors such as the control of centroidal angular momentum (CAM) and the variability of centroidal composite rigid body inertia (CCRBI). This paper introduces a novel integrated dynamic planning and control framework, termed centroidal dynamics model-based model predictive control (CDM-MPC), designed for robust jumping control that fully considers centroidal momentum and non-constant CCRBI. The framework comprises an optimization-based kinodynamic motion planner and an MPC controller for real-time trajectory tracking and replanning. Additionally, a centroidal momentum-based inverse kinematics (IK) solver and a landing heuristic controller are developed to ensure stability during high-impact landings. The efficacy of the CDM-MPC framework is validated through extensive testing on the full-sized humanoid robot KUAVO in both simulations and experiments.