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Periodic Motion Priors for General Quadruped Locomotion Learning
In recent years, advancements in reinforcement learning have achieved remarkable success in quadruped locomotion tasks. Despite their similar structural designs, quadruped robots often require uniquely tailored reward functions for effective motion pattern development, limiting the transferability of learned behaviors across different models. This project proposes to bridge this gap by developing a unified, continuous latent representation of quadruped motions applicable across various robotic platforms. By mapping these motions onto a shared latent space, the project aims to create a versatile foundation that can be adapted to downstream tasks for specific robot configurations.
**Work packages**
Literature research
Motion design and skill development
Skill space development with representation models
**Requirements**
Strong programming skills in Python, C++, and ROS stacks
Experience in reinforcement learning and imitation learning frameworks
Good understanding of autoencoder-based representation learning and generative models
**Publication**
This project will mostly focus on algorithm design and system integration. Promising results will be submitted to robotics or machine learning conferences where outstanding robotic performances are highlighted.
**Related literature**
Peng, Xue Bin, et al. "Deepmimic: Example-guided deep reinforcement learning of physics-based character skills." ACM Transactions On Graphics (TOG) 37.4 (2018): 1-14.
Starke, Sebastian, et al. "Deepphase: Periodic autoencoders for learning motion phase manifolds." ACM Transactions on Graphics (TOG) 41.4 (2022): 1-13.
Iscen, Atil, et al. "Policies modulating trajectory generators." Conference on Robot Learning. PMLR, 2018.
Miki, Takahiro, et al. "Learning robust perceptive locomotion for quadrupedal robots in the wild." Science Robotics 7.62 (2022): eabk2822.
Li, Chenhao, et al. "GLD: Generative latent dynamics for Structured Motion Representation and Learning."
**Work packages**
Literature research
Motion design and skill development
Skill space development with representation models
**Requirements**
Strong programming skills in Python, C++, and ROS stacks
Experience in reinforcement learning and imitation learning frameworks
Good understanding of autoencoder-based representation learning and generative models
**Publication**
This project will mostly focus on algorithm design and system integration. Promising results will be submitted to robotics or machine learning conferences where outstanding robotic performances are highlighted.
**Related literature**
Peng, Xue Bin, et al. "Deepmimic: Example-guided deep reinforcement learning of physics-based character skills." ACM Transactions On Graphics (TOG) 37.4 (2018): 1-14.
Starke, Sebastian, et al. "Deepphase: Periodic autoencoders for learning motion phase manifolds." ACM Transactions on Graphics (TOG) 41.4 (2022): 1-13.
Iscen, Atil, et al. "Policies modulating trajectory generators." Conference on Robot Learning. PMLR, 2018.
Miki, Takahiro, et al. "Learning robust perceptive locomotion for quadrupedal robots in the wild." Science Robotics 7.62 (2022): eabk2822.
Li, Chenhao, et al. "GLD: Generative latent dynamics for Structured Motion Representation and Learning."
Not specified
Please include your CV and transcript in the submission.
**Chenhao Li**
https://breadli428.github.io/
chenhli@ethz.ch
**Takahiro Miki**
https://www.linkedin.com/in/takahiro-miki-a6a065218/?originalSubdomain=ch
tamiki@ethz.ch
Please include your CV and transcript in the submission.
Periodic Motion Priors for General Quadruped Locomotion Learning.pdf
