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About this paper
Haojie Shi, Tingguang Li, Qingxu Zhu, Jiapeng Sheng, Lei Han, Max Q.-H. Meng
Researchers at Tencent Robotics X developed an efficient model-based framework that allows quadrupedal robots to learn agile motor skills without reinforcement, demonstrating a tenfold increase in sample efficiency over PPO in simulation and enabling rapid real-world adaptation in just two minutes of collected data.
Key insights
- A differentiable world model can directly supervise a policy network, allowing for efficient gradient-based updates without explicit reinforcement.
- Iterative adaptation of the world model on real-world data, combined with policy updates through imagined rollouts, effectively bridges the sim-to-real gap with minimal interaction.
- VAE-based policies, when co-trained with a world model, can learn agile and generalized motor skills that imitate complex reference motions and follow high-level commands.
Summary by alphaXiv.
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