MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866913571061891072 |
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| author | Zargarbashi, Fatemeh Di Giuro, Fabrizio Cheng, Jin Kang, Dongho Sukhija, Bhavya Coros, Stelian |
| author_facet | Zargarbashi, Fatemeh Di Giuro, Fabrizio Cheng, Jin Kang, Dongho Sukhija, Bhavya Coros, Stelian |
| contents | This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms. The proposed method trains an RL agent equipped with a memory unit to imitate reference motions using a small set of procedurally generated quadruped robots. Through comprehensive simulation and real-world hardware experiments, we demonstrate the efficacy of our approach in achieving locomotion across various robots without requiring robot-specific fine-tuning. Furthermore, we highlight the critical role of the memory unit in enabling generalization, facilitating rapid adaptation to changes in the robot properties, and improving sample efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17502 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation Zargarbashi, Fatemeh Di Giuro, Fabrizio Cheng, Jin Kang, Dongho Sukhija, Bhavya Coros, Stelian Robotics This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms. The proposed method trains an RL agent equipped with a memory unit to imitate reference motions using a small set of procedurally generated quadruped robots. Through comprehensive simulation and real-world hardware experiments, we demonstrate the efficacy of our approach in achieving locomotion across various robots without requiring robot-specific fine-tuning. Furthermore, we highlight the critical role of the memory unit in enabling generalization, facilitating rapid adaptation to changes in the robot properties, and improving sample efficiency. |
| title | MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.17502 |