RL-based posture-adaptive real-world standing-up control

Determine reinforcement learning formulations and training procedures that can learn posture-adaptive humanoid standing-up controllers which are reliably deployable in real-world environments across diverse initial postures.

Background

The paper highlights that existing approaches to humanoid standing-up control either rely on predefined motion trajectories, which are typically limited to ground-specific postures, or train reinforcement learning agents from scratch, which can yield violent and abrupt motions that hinder deployment on physical hardware.

The task is multi-stage, highly dynamic, and contact-rich, involving time-varying contact points and precise angular momentum control, making conventional RL exploration and optimization difficult. Consequently, achieving posture-adaptive and real-world deployable standing-up control using reinforcement learning is identified as an unresolved challenge.