Achieving Faster and More Accurate Operation of Deep Predictive Learning
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911995504099328 |
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| author | Yoshikawa, Masaki Ito, Hiroshi Ogata, Tetsuya |
| author_facet | Yoshikawa, Masaki Ito, Hiroshi Ogata, Tetsuya |
| contents | Achieving both high speed and precision in robot operations is a significant challenge for social implementation. While factory robots excel at predefined tasks, they struggle with environment-specific actions like cleaning and cooking. Deep learning research aims to address this by enabling robots to autonomously execute behaviors through end-to-end learning with sensor data. RT-1 and ACT are notable examples that have expanded robots' capabilities. However, issues with model inference speed and hand position accuracy persist. High-quality training data and fast, stable inference mechanisms are essential to overcome these challenges. This paper proposes a motion generation model for high-speed, high-precision tasks, exemplified by the sports stacking task. By teaching motions slowly and inferring at high speeds, the model achieved a 94% success rate in stacking cups with a real robot. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10231 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Achieving Faster and More Accurate Operation of Deep Predictive Learning Yoshikawa, Masaki Ito, Hiroshi Ogata, Tetsuya Robotics Achieving both high speed and precision in robot operations is a significant challenge for social implementation. While factory robots excel at predefined tasks, they struggle with environment-specific actions like cleaning and cooking. Deep learning research aims to address this by enabling robots to autonomously execute behaviors through end-to-end learning with sensor data. RT-1 and ACT are notable examples that have expanded robots' capabilities. However, issues with model inference speed and hand position accuracy persist. High-quality training data and fast, stable inference mechanisms are essential to overcome these challenges. This paper proposes a motion generation model for high-speed, high-precision tasks, exemplified by the sports stacking task. By teaching motions slowly and inferring at high speeds, the model achieved a 94% success rate in stacking cups with a real robot. |
| title | Achieving Faster and More Accurate Operation of Deep Predictive Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.10231 |