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arXiv · 2509.06714

RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms

Zakariae El Asri·Ibrahim Laiche·Clément Rambour·Olivier Sigaud·Nicolas Thome
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Abstract

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

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Zakariae El Asri, Ibrahim Laiche, Clément Rambour, Olivier Sigaud, Nicolas Thome. 2025-09-08. RT-HCP: Dealing with Inference Delays and Sample Efficiency to Learn Directly on Robotic Platforms. https://arxiv.org/abs/2509.06714

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