构建可直接用于真实机器人的技能空间，实现复杂抓取任务的高效执行。
Unleashing Humanoid Reaching Potential via Real-world-Ready Skill Space
- 设计一系列真实可用的基础技能，逐个优化并验证跨域迁移能力。
- 将技能整合为统一潜空间，提升任务规划效率与仿真到现实的迁移性。
- 支持零样本仿真到现实迁移，适用于多种复杂抓取场景，适合机器人研发者。
人类在三维世界中拥有广阔的可达空间，能与不同高度和距离的物体交互。然而，在类人机器人上实现这种大范围抓取是复杂的全身控制问题，需同时掌握基座定位与转向、高度与身体姿态调整、末端执行器位姿控制等多种技能。从头学习常导致优化困难且仿真到现实迁移效果差。为此，我们提出真实可用技能空间（R2S2）。方法始于精心设计的、具备真实可用性的基础技能库，通过各技能独立调优及仿真-现实评估确保最优性能与强迁移能力。这些技能被整合进统一的潜在空间，作为结构化先验，使任务执行更高效且具仿真到现实迁移性。高层规划器在此空间采样技能，使机器人完成真实世界的到达任务。我们在多个挑战性目标抓取场景中验证了R2S2，并实现了零样本仿真到现实迁移。
原文摘要 · Abstract (English)
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.
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