无需人类示范，机器人用分层智能体学会打更复杂的结
TWISTED-RL: Hierarchical Skilled Agents for Knot-Tying without Human Demonstrations
- 用分层强化学习替代单步监督学习，以抽象拓扑动作指导打结
- 成功解开复杂结如八字结和单结，成功率提升且规划时间缩短
- 适合研究无需示教的复杂柔性物体操作，如手术或工业装配
机器人打结是机器人学中的基础难题，源于柔体间的复杂交互与严格的拓扑约束。我们提出TWISTED-RL框架，改进了此前无需示教打结的最先进方法（TWISTED）。该方法将单一打结任务分解为可管理的子问题，由专用智能体分别处理。与原方法依赖监督学习的单步逆模型不同，本方案采用基于抽象拓扑动作的多步强化学习策略，实现更精细的拓扑状态转移，避免昂贵低效的数据采集，从而在多种结型间实现更好泛化。实验表明，TWISTED-RL成功解决以往无法完成的高复杂度结，包括常用的八字结和单结。成功率显著提高，规划时间大幅下降，确立了无示教打结的新基准。
原文摘要 · Abstract (English)
Robotic knot-tying represents a fundamental challenge in robotics due to the complex interactions between deformable objects and strict topological constraints. We present TWISTED-RL, a framework that improves upon the previous state-of-the-art in demonstration-free knot-tying (TWISTED), which smartly decomposed a single knot-tying problem into manageable subproblems, each addressed by a specialized agent. Our approach replaces TWISTED's single-step inverse model that was learned via supervised learning with a multi-step Reinforcement Learning policy conditioned on abstract topological actions rather than goal states. This change allows more delicate topological state transitions while avoiding costly and ineffective data collection protocols, thus enabling better generalization across diverse knot configurations. Experimental results demonstrate that TWISTED-RL manages to solve previously unattainable knots of higher complexity, including commonly used knots such as the Figure-8 and the Overhand. Furthermore, the increase in success rates and drop in planning time establishes TWISTED-RL as the new state-of-the-art in robotic knot-tying without human demonstrations.
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