PhD Internship focused on developing cross-embodiment body and hand motion tracking algorithms for humanoids. Engaging in research experiences and possible publication opportunities.
Responsibilities
Creating a workflow to transfer motions from SMPL-X to humanoids (e.g. Unitree G1) in simulation.
Developing an algorithm to perform cross-embodiment motion tracking.
Evaluating the performance of the tracking algorithm.
Combining generating motion models (e.g. diffusion models) to the tracking algorithm.
Requirements
Knowledge of PyTorch, deep learning, neural networks, C++.
Knowledge of good software development practice, e.g. git, documentation.
Knowledge of reinforcement learning and robot physical simulation.
Knowledge of SMPL, SMPL-X, or character animation.
At least one paper in CVPR/ICCV/ECCV/IROS/ICRA or equivalent.
Benefits
Research experiences both in academia and industry.
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