HALO: Hybrid Auto-encoded Locomotion with Learned Latent Dynamics, Poincar\'e Maps, and Regions of Attraction

arXiv cs.RO / 4/22/2026

📰 NewsModels & Research

共有:

Key Points

The paper introduces HALO (Hybrid Auto-encoded Locomotion), a framework to learn latent reduced-order models for periodic hybrid dynamics such as legged-robot locomotion directly from trajectory data.
HALO uses an autoencoder to derive a low-dimensional latent state and a learned latent Poincaré map to model step-to-step locomotion dynamics.
It enables Lyapunov-based stability analysis and estimation of a region of attraction in the latent space, which is then mapped back to the full-order state space via the decoder.
Experiments on a simulated hopping robot and a full-body humanoid show that HALO’s latent models preserve meaningful stability structure and can predict full-order region-of-attraction boundaries.
The work addresses the key challenge of ensuring that stability/safety properties proven or observed in latent space reliably transfer to the original high-dimensional hybrid system.

Abstract

Reduced-order models are powerful for analyzing and controlling high-dimensional dynamical systems. Yet constructing these models for complex hybrid systems such as legged robots remains challenging. Classical approaches rely on hand-designed template models (e.g., LIP, SLIP), which, though insightful, only approximate the underlying dynamics. In contrast, data-driven methods can extract more accurate low-dimensional representations, but it remains unclear when stability and safety properties observed in the latent space meaningfully transfer back to the full-order system. To bridge this gap, we introduce HALO (Hybrid Auto-encoded Locomotion), a framework for learning latent reduced-order models of periodic hybrid dynamics directly from trajectory data. HALO employs an autoencoder to identify a low-dimensional latent state together with a learned latent Poincar\'e map that captures step-to-step locomotion dynamics. This enables Lyapunov analysis and the construction of an associated region of attraction in the latent space, both of which can be lifted back to the full-order state space through the decoder. Experiments on a simulated hopping robot and full-body humanoid locomotion demonstrate that HALO yields low-dimensional models that retain meaningful stability structure and predict full-order region-of-attraction boundaries.