Meta-Reinforcement Learning for Universal Quadrupedal Locomotion Control
<|reference_start|>Meta-Reinforcement Learning for Universal Quadrupedal Locomotion Control: This work presents a deep reinforcement learning-based approach to develop a policy for robot-agnostic locomotion control. Our method involves training an agent equipped with memory, implemented as a recurrent policy, on a diverse set of procedurally generated quadruped robots. We demonstrate that the policies trained by our framework transfer seamlessly to both simulated and real-world quadrupeds not encountered during training, maintaining high-quality motion across platforms. Through a series of simulation and hardware experiments, we highlight the critical role of the recurrent unit in enabling generalization, rapid adaptation to changes in the robot's dynamic properties, and sample efficiency.<|reference_end|>
@article{zargarbashi2024metaloco:,
    title={MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement
  Learning and Motion Imitation},
    author={Fatemeh Zargarbashi, Fabrizio Di Giuro, Jin Cheng, Dongho Kang, Bhavya
  Sukhija and Stelian Coros},
    journal={arXiv preprint arXiv:2407.17502},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17502},
    primaryClass={cs.RO}
}