AutoMate：面向多样几何体的专家与通用装配策略
AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries
AI总结：
AutoMate 提出首个基于仿真的机器人装配学习框架，融合制造工程、角色动画与时间序列分析算法，在 100 种装配任务上学习专家与通用策略，并首次实现大范围零样本 sim-to-real 迁移。
摘要：
机器人装配在高混合生产环境中需要对多样零件和位姿具备适应性，这仍是一个开放性挑战。与此同时，在机器人学的其他领域，大模型和从仿真到现实（sim-to-real）的迁移已带来巨大进展。受此类工作启发，我们提出了 AutoMate，一个学习框架与系统，由 4 个部分组成：1）一个包含 100 种装配任务的数据集，兼容仿真与真实世界，并提供用于策略学习的并行化仿真环境；2）一种新颖的基于仿真的方法，用于学习专家（即零件专用）策略和通用（即统一）装配策略；3）专家策略的演示，在仿真中分别以 80% 或更高的成功率解决 80 种装配任务，以及一个通用策略以 80% 以上的成功率联合解决 20 种装配任务；4）零样本从仿真到现实迁移，实现与仿真相似（或更好）的性能，包括在基于感知初始化的装配任务上。方法上的关键启示是，将来自制造工程、角色动画和时间序列分析的多种算法联合起来，可为多样化的机器人装配问题提供通用且鲁棒的解决方案。据我们所知，AutoMate 提供了首个基于仿真的框架，用于在广泛的装配任务范围内学习专家和通用策略，同时也是首个在此类范围内展示零样本从仿真到现实迁移的系统。视频和更多细节请参见我们的项目网站：https://bingjietang718.github.io/automate/
英文摘要：
Robotic assembly for high-mixture settings requires adaptivity to diverse parts and poses, which is an open challenge. Meanwhile, in other areas of robotics, large models and sim-to-real have led to tremendous progress. Inspired by such work, we present AutoMate, a learning framework and system that consists of 4 parts: 1) a dataset of 100 assemblies compatible with simulation and the real world, along with parallelized simulation environments for policy learning, 2) a novel simulation-based approach for learning specialist (i.e., part-specific) policies and generalist (i.e., unified) assembly policies, 3) demonstrations of specialist policies that individually solve 80 assemblies with 80% or higher success rates in simulation, as well as a generalist policy that jointly solves 20 assemblies with an 80%+ success rate, and 4) zero-shot sim-to-real transfer that achieves similar (or better) performance than simulation, including on perception-initialized assembly. The key methodological takeaway is that a union of diverse algorithms from manufacturing engineering, character animation, and time-series analysis provides a generic and robust solution for a diverse range of robotic assembly problems. To our knowledge, AutoMate provides the first simulation-based framework for learning specialist and generalist policies over a wide range of assemblies, as well as the first system demonstrating zero-shot sim-to-real transfer over such a range. For videos and additional details, please see our project website: https://bingjietang718.github.io/automate/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
