让机器人从混乱中学习拼图，无需先验知识即可泛化组装新物体。
Master Rules from Chaos: Learning to Reason, Plan, and Interact from Chaos for Tangram Assembly
- 通过模拟自探索学习组装策略，不依赖几何模型或人工标注。
- 仅用轮廓提示就能完成训练未见的新拼图对象组装，表现鲁棒。
- 适合研究机器人泛化能力、自主规划与视觉反馈控制的场景。
拼图组装是体现人类智能与操作灵巧性的挑战，对当前机器人技术构成新考验。本文介绍首次探索，并揭示了在推理、规划与操作方面的关键问题。提出MRChaos（从混沌中掌握规则），一种可泛化到新物体的鲁棒通用解决方案。不同于依赖先验几何与运动学模型的传统方法，MRChaos通过在仿真中自探索学习随机生成物体的组装策略，奖励信号来自视觉观测变化，无需手动设计模型或标注。该方法在仅提供轮廓提示的情况下，仍能成功组装训练中未遇过的多种新颖拼图对象。实验还展示了其在餐具组合等更广泛应用中的潜力。研究表明，通过在更简单领域中学习，可实现机器人组装任务的激进泛化。
原文摘要 · Abstract (English)
Tangram assembly, the art of human intelligence and manipulation dexterity, is a new challenge for robotics and reveals the limitations of state-of-the-arts. Here, we describe our initial exploration and highlight key problems in reasoning, planning, and manipulation for robotic tangram assembly. We present MRChaos (Master Rules from Chaos), a robust and general solution for learning assembly policies that can generalize to novel objects. In contrast to conventional methods based on prior geometric and kinematic models, MRChaos learns to assemble randomly generated objects through self-exploration in simulation without prior experience in assembling target objects. The reward signal is obtained from the visual observation change without manually designed models or annotations. MRChaos retains its robustness in assembling various novel tangram objects that have never been encountered during training, with only silhouette prompts. We show the potential of MRChaos in wider applications such as cutlery combinations. The presented work indicates that radical generalization in robotic assembly can be achieved by learning in much simpler domains.
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