核心概念
The author proposes an efficient model-based learning framework to enhance sample efficiency and address the sim-to-real gap in acquiring agile motor skills for quadrupedal robots.
摘要
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.
Key points include:
- Proposal of an efficient model-based learning framework for acquiring agile motor skills in quadrupedal robots.
- Addressing challenges related to sim-to-real gap and low sample efficiency.
- Combining a world model with a policy network to reduce reliance on real interaction data.
- Achieving significant improvements in sample efficiency compared to traditional reinforcement learning methods.
- Conducting experiments in both simulated and real-world environments to validate the approach.
統計資料
Our simulated results show a tenfold sample efficiency increase compared to reinforcement learning methods such as PPO.
In real-world testing, our policy achieves proficient command-following performance with only a two-minute data collection period.
引述
"Learning-based methods have improved locomotion skills of quadruped robots through deep reinforcement learning."
"Our simulated results show a tenfold sample efficiency increase compared to reinforcement learning methods such as PPO."