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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.08028 |
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| _version_ | 1866910550151135232 |
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| author | Tang, Bingjie Akinola, Iretiayo Xu, Jie Wen, Bowen Handa, Ankur Van Wyk, Karl Fox, Dieter Sukhatme, Gaurav S. Ramos, Fabio Narang, Yashraj |
| author_facet | Tang, Bingjie Akinola, Iretiayo Xu, Jie Wen, Bowen Handa, Ankur Van Wyk, Karl Fox, Dieter Sukhatme, Gaurav S. Ramos, Fabio Narang, Yashraj |
| contents | 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/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08028 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries Tang, Bingjie Akinola, Iretiayo Xu, Jie Wen, Bowen Handa, Ankur Van Wyk, Karl Fox, Dieter Sukhatme, Gaurav S. Ramos, Fabio Narang, Yashraj Robotics 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/ |
| title | AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.08028 |