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Learning Real-time Human Motion Tracking on a Humanoid Robot
Humanoid robots, designed to mimic the structure and behavior of humans, have seen significant advancements in kinematics, dynamics, and control systems. Teleoperation of humanoid robots involves complex control strategies to manage bipedal locomotion, balance, and interaction with environments. Research in this area has focused on developing robots that can perform tasks in environments designed for humans, from simple object manipulation to navigating complex terrains. Reinforcement learning has emerged as a powerful method for enabling robots to learn from interactions with their environment, improving their performance over time without explicit programming for every possible scenario. In the context of humanoid robotics and teleoperation, RL can be used to optimize control policies, adapt to new tasks, and improve the efficiency and safety of human-robot interactions. Key challenges include the high dimensionality of the action space, the need for safe exploration, and the transfer of learned skills across different tasks and environments. Integrating human motion tracking with reinforcement learning on humanoid robots represents a cutting-edge area of research. This approach involves using human motion data as input to train RL models, enabling the robot to learn more natural and human-like movements. The goal is to develop systems that can not only replicate human actions in real-time but also adapt and improve their responses over time through learning. Challenges in this area include ensuring real-time performance, dealing with the variability of human motion, and maintaining stability and safety of the humanoid robot.
**Work packages**
Literature research
Human motion capture and retargeting
Skill space development
Hardware validation encouraged upon availability
**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.
Li, Chenhao, et al. "FLD: Fourier latent dynamics for Structured Motion Representation and Learning."
Winkler, A., et al. "QuestSim: Human motion tracking from sparse sensors with simulated avatars." In SIGGRAPH Asia 2022 Conference Papers (pp. 1-8).
**Work packages**
Literature research
Human motion capture and retargeting
Skill space development
Hardware validation encouraged upon availability
**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.
Li, Chenhao, et al. "FLD: Fourier latent dynamics for Structured Motion Representation and Learning."
Winkler, A., et al. "QuestSim: Human motion tracking from sparse sensors with simulated avatars." In SIGGRAPH Asia 2022 Conference Papers (pp. 1-8).
Not specified
Please include your CV and transcript in the submission.
**Chenhao Li**
https://breadli428.github.io/
chenhli@ethz.ch
**Junzhe He**
https://www.linkedin.com/in/junzhe-he-05aa8022a/?originalSubdomain=ch
junzhe@ethz.ch
Please include your CV and transcript in the submission.
