RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916939830394880 |
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| author | Asri, Zakariae El Laiche, Ibrahim Rambour, Clément Sigaud, Olivier Thome, Nicolas |
| author_facet | Asri, Zakariae El Laiche, Ibrahim Rambour, Clément Sigaud, Olivier Thome, Nicolas |
| contents | Learning a controller directly on the robot requires extreme sample efficiency. Model-based reinforcement learning (RL) methods are the most sample efficient, but they often suffer from a too long inference time to meet the robot control frequency requirements. In this paper, we address the sample efficiency and inference time challenges with two contributions. First, we define a general framework to deal with inference delays where the slow inference robot controller provides a sequence of actions to feed the control-hungry robotic platform without execution gaps. Then, we compare several RL algorithms in the light of this framework and propose RT-HCP, an algorithm that offers an excellent trade-off between performance, sample efficiency and inference time. We validate the superiority of RT-HCP with experiments where we learn a controller directly on a simple but high frequency FURUTA pendulum platform. Code: github.com/elasriz/RTHCP |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06714 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms Asri, Zakariae El Laiche, Ibrahim Rambour, Clément Sigaud, Olivier Thome, Nicolas Machine Learning Learning a controller directly on the robot requires extreme sample efficiency. Model-based reinforcement learning (RL) methods are the most sample efficient, but they often suffer from a too long inference time to meet the robot control frequency requirements. In this paper, we address the sample efficiency and inference time challenges with two contributions. First, we define a general framework to deal with inference delays where the slow inference robot controller provides a sequence of actions to feed the control-hungry robotic platform without execution gaps. Then, we compare several RL algorithms in the light of this framework and propose RT-HCP, an algorithm that offers an excellent trade-off between performance, sample efficiency and inference time. We validate the superiority of RT-HCP with experiments where we learn a controller directly on a simple but high frequency FURUTA pendulum platform. Code: github.com/elasriz/RTHCP |
| title | RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.06714 |