AHMP: Agile Humanoid Motion Planning with Contact Sequence Discovery
Ioannis Tsikelis, Evangelos Tsiatsianas, Chairi Kiourt, Serena Ivaldi, Konstantinos I. Chatzilygeroudis, Enrico Mingo Hoffman · 2025
Planning agile whole-body motions for legged and humanoid robots is a fundamental requirement for enabling dynamic tasks such as running, jumping, and fast reactive maneuvers. In this work, we present AHMP, a multi-contact motion planning framework based on bi-level optimization that integrates a contact sequence discovery technique, using the Mixed-Distribution Cross-Entropy Method (CEM-MD), and an efficient trajectory optimization scheme, which parameterizes the robot's poses and motions in the tangent space of$\operatorname{SE}(3)$. AHMP permits the automatic generation of feasible contact configurations, with associated whole-body dynamic transitions. We validate our approach on a set of challenging agile motion planning tasks for humanoid robots, demonstrating that contact sequence discovery combined with tangent space parameterization leads to highly dynamic motion plans while remaining computationally efficient.