Unleashing Humanoid Reaching Potential Via Real-World-Ready Skill Space
Zhikai Zhang, Chao Chen, Han Xue, Jilong Wang, Sikai Liang, Yun Liu, Zongzhang Zhang, He Wang
Abstract
Humans possess a large reachable space in the 3D world, enabling interactions with objects at varying heights and distances. However, realizing such large-space reaching on humanoids is a complex whole-body control (WBC) problem. Learning from scratch often leads to optimization difficulty and poor sim2real transferability. To address these challenges, we present Real-world-Ready Skill Space (R2S2), a structural skill prior that helps autonomous whole-body-control task execution in an efficient manner while maintaining sim2real transferability. Inheriting knowledge from a set of real-world-ready primitive skills to ease multi-skill learning, R2S2 further expands the capability of primitive skills and learns a unified structural skill representation. By sampling from R2S2, we unleash humanoid reaching potential in many real-world tasks. As a beneficial side effect, R2S2 can also support humanoid whole-body teleoperation with a large reachable space. We validate the generalizability of R2S2 in various challenging goal-reaching tasks across different robot platforms, simulation and real world.