An Efficient Model-Based Approach on Learning Agile Motor Skills without Reinforcement
arxiv(2024)
摘要
Learning-based methods have improved locomotion skills of quadruped robots
through deep reinforcement learning. However, the sim-to-real gap and low
sample efficiency still limit the skill transfer. To address this issue, we
propose an efficient model-based learning framework that combines a world model
with a policy network. We train a differentiable world model to predict future
states and use it to directly supervise a Variational Autoencoder (VAE)-based
policy network to imitate real animal behaviors. This significantly reduces the
need for real interaction data and allows for rapid policy updates. We also
develop a high-level network to track diverse commands and trajectories. 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 and generalizes well to new speeds and paths.
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