DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

TL;DR

DynaMo introduces an in-domain self-supervised approach for learning visual representations from expert demonstrations, significantly enhancing imitation learning efficiency without relying on out-of-domain data or complex augmentations.

Contribution

DynaMo jointly learns latent inverse and forward dynamics models from in-domain data, improving visual representation quality for visuomotor control tasks.

Findings

DynaMo outperforms prior self-supervised methods in imitation learning tasks.

Representation quality improves across various policy architectures.

Ablation studies highlight key components contributing to performance gains.

Abstract

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