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Continuous Skill Learning with Fourier Latent Dynamics
In recent years, advancements in reinforcement learning have achieved remarkable success in teaching robots discrete motor skills. However, this process often involves intricate reward structuring and extensive hyperparameter adjustments for each new skill, making it a time-consuming and complex endeavor. This project proposes the development of a skill generator operating within a continuous latent space. This innovative approach contrasts with the discrete skill learning methods currently prevalent in the field. By leveraging a continuous latent space, the skill generator aims to produce a diverse range of skills without the need for individualized reward designs and hyperparameter configurations for each skill. This method not only simplifies the skill generation process but also promises to enhance the adaptability and efficiency of skill learning in robotics.
**Work packages**
Literature research
Motion design and skill development
Skill space development with representation models
Method evaluation with ablation studies
Hardware validation is encouraged
**Requirements**
Strong programming skills in Python
Experience in reinforcement learning and imitation learning frameworks
**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. "FLD: Fourier latent dynamics for Structured Motion Representation and Learning."
**Work packages**
Literature research
Motion design and skill development
Skill space development with representation models
Method evaluation with ablation studies
Hardware validation is encouraged
**Requirements**
Strong programming skills in Python
Experience in reinforcement learning and imitation learning frameworks
**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. "FLD: Fourier 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
**Nikita Rudin**
https://www.linkedin.com/in/nikita-rudin-bb3199121/?originalSubdomain=ch
rudinn@ethz.ch
Please include your CV and transcript in the submission.
Continuous Skill Learning with Generative Latent Dynamics.pdf
