Adaptive Mobile Manipulation for Articulated Objects In the Open World
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917576850800640 |
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| author | Xiong, Haoyu Mendonca, Russell Shaw, Kenneth Pathak, Deepak |
| author_facet | Xiong, Haoyu Mendonca, Russell Shaw, Kenneth Pathak, Deepak |
| contents | Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14403 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Mobile Manipulation for Articulated Objects In the Open World Xiong, Haoyu Mendonca, Russell Shaw, Kenneth Pathak, Deepak Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Systems and Control Deploying robots in open-ended unstructured environments such as homes has been a long-standing research problem. However, robots are often studied only in closed-off lab settings, and prior mobile manipulation work is restricted to pick-move-place, which is arguably just the tip of the iceberg in this area. In this paper, we introduce Open-World Mobile Manipulation System, a full-stack approach to tackle realistic articulated object operation, e.g. real-world doors, cabinets, drawers, and refrigerators in open-ended unstructured environments. The robot utilizes an adaptive learning framework to initially learns from a small set of data through behavior cloning, followed by learning from online practice on novel objects that fall outside the training distribution. We also develop a low-cost mobile manipulation hardware platform capable of safe and autonomous online adaptation in unstructured environments with a cost of around 20,000 USD. In our experiments we utilize 20 articulate objects across 4 buildings in the CMU campus. With less than an hour of online learning for each object, the system is able to increase success rate from 50% of BC pre-training to 95% using online adaptation. Video results at https://open-world-mobilemanip.github.io/ |
| title | Adaptive Mobile Manipulation for Articulated Objects In the Open World |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2401.14403 |