Humanoid Goalkeeper: Learning from Position Conditioned Task-Motion Constraints

TL;DR

This paper introduces a reinforcement learning framework enabling humanoid robots to perform autonomous, naturalistic goalkeeping and related tasks by learning a unified policy conditioned on position and motion constraints, demonstrated through real-world experiments.

Contribution

It presents a novel end-to-end RL approach that integrates human motion priors conditioned on perceptual inputs for humanoid goalkeeping, surpassing prior methods reliant on teleoperation or fixed motions.

Findings

Successful real-world autonomous goalkeeping with natural motions

Effective generalization to tasks like ball escaping and grabbing

Demonstrated agility and responsiveness in dynamic scenarios

Abstract

We present a reinforcement learning framework for autonomous goalkeeping with humanoid robots in real-world scenarios. While prior work has demonstrated similar capabilities on quadrupedal platforms, humanoid goalkeeping introduces two critical challenges: (1) generating natural, human-like whole-body motions, and (2) covering a wider guarding range with an equivalent response time. Unlike existing approaches that rely on separate teleoperation or fixed motion tracking for whole-body control, our method learns a single end-to-end RL policy, enabling fully autonomous, highly dynamic, and human-like robot-object interactions. To achieve this, we integrate multiple human motion priors conditioned on perceptual inputs into the RL training via an adversarial scheme. We demonstrate the effectiveness of our method through real-world experiments, where the humanoid robot successfully performs agile, autonomous, and naturalistic interceptions of fast-moving balls. In addition to goalkeeping, we demonstrate the generalization of our approach through tasks such as ball escaping and grabbing. Our work presents a practical and scalable solution for enabling highly dynamic interactions between robots and moving objects, advancing the field toward more adaptive and lifelike robotic behaviors.