Spherical Latent Motion Prior for Physics-Based Simulated Humanoid Control

TL;DR

This paper introduces the Spherical Latent Motion Prior (SLMP), a novel two-stage approach that learns structured motion priors for humanoid control, enabling stable, diverse, and realistic behaviors in physics-based simulations.

Contribution

SLMP combines motion tracking and distillation into a spherical latent space, overcoming limitations of VAE and AMP, and enabling stable, diverse motion sampling for humanoid control.

Findings

SLMP preserves fine motion details without information loss.

Random sampling in SLMP produces semantically valid behaviors.

SLMP generalizes across different humanoid morphologies.

Abstract

Learning motion priors for physics-based humanoid control is an active research topic. Existing approaches mainly include variational autoencoders (VAE) and adversarial motion priors (AMP). VAE introduces information loss, and random latent sampling may sometimes produce invalid behaviors. AMP suffers from mode collapse and struggles to capture diverse motion skills. We present the Spherical Latent Motion Prior (SLMP), a two-stage method for learning motion priors. In the first stage, we train a high-quality motion tracking controller. In the second stage, we distill the tracking controller into a spherical latent space. A combination of distillation, a discriminator, and a discriminator-guided local semantic consistency constraint shapes a structured latent action space, allowing stable random sampling without information loss. To evaluate SLMP, we collect a two-hour human combat motion capture dataset and show that SLMP preserves fine motion detail without information loss, and random sampling yields semantically valid and stable behaviors. When applied to a two-agent physics-based combat task, SLMP produces human-like and physically plausible combat behaviors only using simple rule-based rewards. Furthermore, SLMP generalizes across different humanoid robot morphologies, demonstrating its transferability beyond a single simulated avatar.