Unleashing Humanoid Reaching Potential via Real-world-Ready Skill Space

2025-05-16

AI Summary

This work addresses core challenges in achieving robust, generalizable goal-reaching for humanoid robots in large 3D environments: tight coupling among base localization/reorientation, whole-body pose regulation, and end-effector pose control; slow zero-shot training convergence; and poor sim-to-real transfer. We propose the Real-World-Ready Skill Space (R2S2), which embeds empirically validated atomic skills into a structured implicit skill space to form a unified, prior-enriched representation. R2S2 integrates a sampling-based high-level planner with a joint reinforcement learning–motion control optimization framework to enable end-to-end goal-reaching. Experiments demonstrate zero-shot sim-to-real transfer across complex scenarios—including multi-height terrains, long-distance navigation, and dynamic obstacles—while significantly improving task success rate, generalization, and robustness.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Humans possess a large reachable space in the 3D world, enabling interaction with objects at varying heights and distances. However, realizing such large-space reaching on humanoids is a complex whole-body control problem and requires the robot to master diverse skills simultaneously-including base positioning and reorientation, height and body posture adjustments, and end-effector pose control. Learning from scratch often leads to optimization difficulty and poor sim2real transferability. To address this challenge, we propose Real-world-Ready Skill Space (R2S2). Our approach begins with a carefully designed skill library consisting of real-world-ready primitive skills. We ensure optimal performance and robust sim2real transfer through individual skill tuning and sim2real evaluation. These skills are then ensembled into a unified latent space, serving as a structured prior that helps task execution in an efficient and sim2real transferable manner. A high-level planner, trained to sample skills from this space, enables the robot to accomplish real-world goal-reaching tasks. We demonstrate zero-shot sim2real transfer and validate R2S2 in multiple challenging goal-reaching scenarios.

Problem

• Achieving large-space reaching on humanoids via whole-body control
• Overcoming optimization and sim2real transfer challenges in skill learning
• Enabling real-world goal-reaching with a unified skill space

Innovation

• Real-world-ready primitive skill library
• Unified latent space for skill ensembling
• High-level planner for skill sampling