  There are several challenges in developing a model for multi-tasking humanoid
control. Reinforcement learning and imitation learning approaches are quite
popular in this domain. However, there is a trade-off between the two.
Reinforcement learning is not the best option for training a humanoid to
perform multiple behaviors due to training time and model size, and imitation
learning using kinematics data alone is not appropriate to realize the actual
physics of the motion. Training models to perform multiple complex tasks take
long training time due to high DoF and complexities of the movements. Although
training models offline would be beneficial, another issue is the size of the
dataset, usually being quite large to encapsulate multiple movements. There are
few implementations of transformer-based models to control humanoid characters
and predict their motion based on a large dataset of recorded/reference motion.
In this paper, we train a GPT on a large dataset of noisy expert policy rollout
observations from a humanoid motion dataset as a pre-trained model and fine
tune that model on a smaller dataset of noisy expert policy rollout
observations and actions to autoregressively generate physically plausible
motion trajectories. We show that it is possible to train a GPT-based
foundation model on a smaller dataset in shorter training time to control a
humanoid in a realistic physics environment to perform human-like movements.
