An efficient model-based learning approach is introduced to improve sample efficiency and bridge the sim-to-real gap in acquiring agile motor skills for quadruped robots. The framework combines a world model with a policy network, significantly reducing the need for real interaction data. Results show a tenfold increase in sample efficiency compared to reinforcement learning methods like PPO, with proficient command-following performance achieved in real-world testing after just a two-minute data collection period.
The content discusses the challenges of transferring model-free reinforcement learning policies from simulation to reality and presents an alternative approach of training or fine-tuning policies directly on real robots. By training both the world model and control policy in a supervised manner, the method enhances sample efficiency and allows for rapid policy updates. The paper also highlights experiments conducted in simulation environments and real-world scenarios to evaluate the effectiveness of the proposed framework.
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by Haojie Shi,T... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.01962.pdfDeeper Inquiries