title: DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control
summary: Imitation learning has proven to be a powerful tool for training complex
  visuomotor policies. However, current methods often require hundreds to
  thousands of expert demonstrations to handle high-dimensional visual
  observations. A key reason for this poor data efficiency is that visual
  representations are predominantly either pretrained on out-of-domain data or
  trained directly through a behavior cloning objective. In this work, we present
  DynaMo, a new in-domain, self-supervised method for learning visual
  representations. Given a set of expert demonstrations, we jointly learn a
  latent inverse dynamics model and a forward dynamics model over a sequence of
  image embeddings, predicting the next frame in latent space, without
  augmentations, contrastive sampling, or access to ground truth actions.
  Importantly, DynaMo does not require any out-of-domain data such as Internet
  datasets or cross-embodied datasets. On a suite of six simulated and real
  environments, we show that representations learned with DynaMo significantly
  improve downstream imitation learning performance over prior self-supervised
  learning objectives, and pretrained representations. Gains from using DynaMo
  hold across policy classes such as Behavior Transformer, Diffusion Policy, MLP,
  and nearest neighbors. Finally, we ablate over key components of DynaMo and
  measure its impact on downstream policy performance. Robot videos are best
  viewed at https://dynamo-ssl.github.io
paper_authors._id: 66f36d09ad897b1a4509442b
paper_authors.name: Zichen Jeff Cui
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-12-23T20:49:42.648Z
paper_authors._id: 6769cca04366e7d8be14b0da
paper_authors.avatarUrl: /avatars/87e3e3ae7dab1306e7d9ab12a1480f69.svg
paper_authors.fullname: Jeff Cui
paper_authors.type: user
paper_authors.user: jeffacce
paper_authors._id: 66f36d09ad897b1a4509442c
paper_authors.name: Hengkai Pan
paper_authors._id: 66f36d09ad897b1a4509442d
paper_authors.name: Aadhithya Iyer
paper_authors._id: 66f36d09ad897b1a4509442e
paper_authors.name: Siddhant Haldar
paper_authors._id: 66f36d09ad897b1a4509442f
paper_authors.name: Lerrel Pinto
paper_title: DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control
paper_summary: Imitation learning has proven to be a powerful tool for training complex
  visuomotor policies. However, current methods often require hundreds to
  thousands of expert demonstrations to handle high-dimensional visual
  observations. A key reason for this poor data efficiency is that visual
  representations are predominantly either pretrained on out-of-domain data or
  trained directly through a behavior cloning objective. In this work, we present
  DynaMo, a new in-domain, self-supervised method for learning visual
  representations. Given a set of expert demonstrations, we jointly learn a
  latent inverse dynamics model and a forward dynamics model over a sequence of
  image embeddings, predicting the next frame in latent space, without
  augmentations, contrastive sampling, or access to ground truth actions.
  Importantly, DynaMo does not require any out-of-domain data such as Internet
  datasets or cross-embodied datasets. On a suite of six simulated and real
  environments, we show that representations learned with DynaMo significantly
  improve downstream imitation learning performance over prior self-supervised
  learning objectives, and pretrained representations. Gains from using DynaMo
  hold across policy classes such as Behavior Transformer, Diffusion Policy, MLP,
  and nearest neighbors. Finally, we ablate over key components of DynaMo and
  measure its impact on downstream policy performance. Robot videos are best
  viewed at https://dynamo-ssl.github.io
paper_ai_summary: DynaMo, a self-supervised method for learning in-domain visual representations, enhances imitation learning by jointly training latent inverse and forward dynamics models without requiring out-of-domain data.
paper_ai_keywords: latent inverse dynamics model forward dynamics model self-supervised learning imitation learning image embeddings Behavior Transformer Diffusion Policy nearest neighbors