Kinodynamic Motion Planning for Multi-Legged Robot Jumping via Mixed-Integer Convex Program
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Abstract (arXiv)
This paper proposes a kinodynamic motion planning framework for multi-legged robot jumping based on the mixed-integer convex program (MICP), which simultaneously reasons about centroidal motion, contact points, wrench, and gait sequences. This method uniquely combines configuration space discretization and the construction of feasible wrench polytope (FWP) to encode kinematic constraints, actuator limit, friction cone constraint, and gait sequencing into a single MICP. The MICP could be efficiently solved to the global optimum by off-the-shelf numerical solvers and provide highly dynamic jumping motions without requiring initial guesses. Simulation and experimental results demonstrate that the proposed method could find novel and dexterous maneuvers that are directly deployable on the two-legged robot platform to traverse through challenging terrains.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). First posted on arXiv 3 Nov 2020.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1.
4 papers.
- 2023Robust and Versatile Bipedal Jumping Control through Reinforcement Learningcited by 27
- 2023RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotioncited by 14
- 2024Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?cited by 2
- 2025Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxationscited by 1
- 2025Hierarchical Contact-Rich Trajectory Optimization for Multi-Modal Manipulation using Tight Convex Relaxations11 Mar 2025
- 2024Is Linear Feedback on Smoothed Dynamics Sufficient for Stabilizing Contact-Rich Plans?10 Nov 2024
- 2023RL + Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion29 May 2023
- 2023Robust and Versatile Bipedal Jumping Control through Reinforcement Learning19 Feb 2023