机器人可自主装配多种零件，无需示教就能完成复杂组装。
Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning
- 分层规划+自动夹具生成，实现长时序多步装配
- 轻量强化学习使真实世界成功率达80%
- 首个无需领域知识的通用双臂装配系统
多部件装配对机器人而言是极具挑战的任务，需要在长时序、高接触性操作中具备泛化能力。本文提出Fabrica，一个双臂机器人系统，能够端到端地完成通用多部件物体的自主装配。通过构建包含优先级、序列、抓取和运动规划的分层体系，并结合自动化夹具生成，该系统可在任意双臂机器人上实现多步骤装配。规划器采用可并行设计，提升效率并优化控制稳定性。针对高接触性装配步骤，提出一种轻量级强化学习框架，基于等变性和规划得到的残差动作，在不同几何形状、装配方向和抓取姿态下训练通用策略，零样本迁移至真实场景，实现80%的成功率。为系统评估，构建了一个涵盖工业与日常物品的多部件装配基准套件。通过高效全局规划与鲁棒局部控制的集成，首次实现无需领域知识或人类示范的完整且可泛化的现实世界多部件装配。
原文摘要 · Abstract (English)
Multi-part assembly poses significant challenges for robots to execute long-horizon, contact-rich manipulation with generalization across complex geometries. We present Fabrica, a dual-arm robotic system capable of end-to-end planning and control for autonomous assembly of general multi-part objects. For planning over long horizons, we develop hierarchies of precedence, sequence, grasp, and motion planning with automated fixture generation, enabling general multi-step assembly on any dual-arm robots. The planner is made efficient through a parallelizable design and is optimized for downstream control stability. For contact-rich assembly steps, we propose a lightweight reinforcement learning framework that trains generalist policies across object geometries, assembly directions, and grasp poses, guided by equivariance and residual actions obtained from the plan. These policies transfer zero-shot to the real world and achieve 80% successful steps. For systematic evaluation, we propose a benchmark suite of multi-part assemblies resembling industrial and daily objects across diverse categories and geometries. By integrating efficient global planning and robust local control, we showcase the first system to achieve complete and generalizable real-world multi-part assembly without domain knowledge or human demonstrations. Project website: http://fabrica.csail.mit.edu/
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