Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained
  Transformers
Authors: name: Lirui Wang, name: Xinlei Chen, name: Jialiang Zhao, name: Kaiming He

One of the roadblocks for training generalist robotic models today is
heterogeneity. Previous robot learning methods often collect data to train with
one specific embodiment for one task, which is expensive and prone to
overfitting. This work studies the problem of learning policy representations
through heterogeneous pre-training on robot data across different embodiments
and tasks at scale. We propose Heterogeneous Pre-trained Transformers (HPT),
which pre-train a large, shareable trunk of a policy neural network to learn a
task and embodiment agnostic shared representation. This general architecture
aligns the specific proprioception and vision inputs from distinct embodiments
to a short sequence of tokens and then processes such tokens to map to control
robots for different tasks. Leveraging the recent large-scale multi-embodiment
real-world robotic datasets as well as simulation, deployed robots, and human
video datasets, we investigate pre-training policies across heterogeneity. We
conduct experiments to investigate the scaling behaviors of training
objectives, to the extent of 52 datasets. HPTs outperform several baselines and
enhance the fine-tuned policy performance by over 20% on unseen tasks in
multiple simulator benchmarks and real-world settings. See the project website
(https://liruiw.github.io/hpt/) for code and videos.