title: Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained
    Transformers
summary: 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.
paper_authors._id: 66fbeb7863002e1de024034d
paper_authors.name: Lirui Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-01T13:32:33.645Z
paper_authors._id: 63151385b031f7b1c7c0871c
paper_authors.avatarUrl: /avatars/0088eb929866face5f95218943e3f478.svg
paper_authors.fullname: Lirui Wang
paper_authors.type: user
paper_authors.user: liruiw
paper_authors._id: 66fbeb7863002e1de024034e
paper_authors.name: Xinlei Chen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-10-01T13:32:43.210Z
paper_authors._id: 63e58e3a006a775275e59e41
paper_authors.avatarUrl: /avatars/75262a35b27a2ae1939df9118120d99e.svg
paper_authors.fullname: Xinlei Chen
paper_authors.type: user
paper_authors.user: endernewton
paper_authors._id: 66fbeb7863002e1de024034f
paper_authors.name: Jialiang Zhao
paper_authors._id: 66fbeb7863002e1de0240350
paper_authors.name: Kaiming He
paper_title: Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained
    Transformers
paper_summary: 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.
paper_ai_summary: Heterogeneous Pre-trained Transformers enhance general robot model capabilities by learning a shared representation across diverse robotic tasks and embodiments.
paper_ai_keywords: Heterogeneous Pre-trained Transformers task and embodiment agnostic proprioception vision inputs tokens control robots large-scale multi-embodiment datasets simulator benchmarks