Hold My Beer: Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control

Yitang Li, Yuanhang Zhang, Wenli Xiao, Chaoyi Pan, Haoyang Weng, Guanqi He, Tairan He, Guanya Shi

Keywords: Humanoid Robots, Reinforcement Learning, Stable Locomotion

TL;DR: We decouple humanoid control into high-frequency arm and low-frequency leg policies, enabling stable end-effector stable control during dynamic locomotion.

Abstract: Can your humanoid walk up and hand you a full cup of beer—without spilling a drop? While humanoids are increasingly featured in flashy demos—dancing, delivering packages, traversing rough terrain—fine-grained control during locomotion remains a significant challenge. In particular, stabilizing a filled end-effector (EE) while walking is far from solved, due to a fundamental mismatch in task dynamics: locomotion demands slow-timescale, robust control, whereas EE stabilization requires rapid, high-precision corrections. To address this, we propose SoFTA, a Slow-Fast Two-Agent framework that decouples upper-body and lower-body control into separate agents operating at different frequencies and with distinct rewards. This temporal and objective separation mitigates policy interference mitagates objective conflict and enables coordinated whole-body behavior. SoFTA executes upper-body actions at 100 Hz for precise EE control and lower-body actions at 50 Hz for robust gait. It reduces EE acceleration by 2-5x to baselines and performs 2–3x closer to human-level stability, enabling delicate tasks such as carrying nearly full cups, capturing steady video during locomotion, and disturbance rejection with EE stability.

Submission Number: 283