DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control (2409.12192v2)

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

• The paper introduces DynaMo, a novel pretraining method combining inverse and forward dynamics models to enhance visuo-motor control.
• It eliminates data augmentations by learning directly from in-domain action sequences, streamlining the self-supervised training process.
• Experiments across simulated and real-world tasks demonstrate a 39% improvement over previous self-supervised approaches.

An Overview of DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control

The paper "DynaMo: In-Domain Dynamics Pretraining for Visuo-Motor Control" presents an approach to enhance the efficiency of imitation learning in visuo-motor control tasks by employing in-domain, self-supervised pretraining. This is particularly aimed at addressing limitations in the current state of visual representations, which typically depend on large-scale out-of-domain data or on data specifically processed through behavior cloning objectives. The authors introduce a novel self-supervised method, DynaMo, which is distinctive for its incorporation of both inverse and forward dynamics models. These models, operating within a sequence of image embeddings, learn to predict future frames in latent space without relying on augmentations or ground truth actions.

Key Contributions and Techniques

The paper underscores several contributions:

1. In-Domain Dynamics Modeling: DynaMo is proposed as a method for pretraining visual representations directly using limited in-domain data. By focusing on the sequence of demonstrated actions, DynaMo exploits the natural causal structure inherent in visuo-motor demonstrations. It effectively integrates an encoder with both forward and inverse dynamics models to learn the dynamics subtleties in the observational data.
2. Elimination of Augmentations: Unlike prevalent self-supervised methods reliant on augmentations or complex sampling strategies, DynaMo operates without these, streamlining the training process and focusing purely on the dynamics prediction task within the latent space.
3. Empirical Validation Across Diverse Environments: The research validates the effectiveness of DynaMo through rigorous testing across four simulated environments and two real-world robotic tasks, including Block Pushing and xArm Kitchen environments. Performance assessments reveal that DynaMo representations notably enhance downstream imitation learning performance. Specifically, DynaMo achieved a 39% improvement over previous state-of-the-art self-supervised approaches on challenging closed-loop and real-robot tasks based on their reported metrics.
4. Ablation and Component Impact Studies: The paper includes a thorough examination of DynaMo’s components, emphasizing their contributions to final task performance, and evaluates different policy classes to demonstrate DynaMo's adaptability and efficacy in various scenarios.

Implications and Future Prospects

The insights from this research carry significant implications both in practice and theory. Practically, the findings indicate that in-domain dynamics pretraining can substantially improve policy performance in data-constrained visuo-motor tasks. This leads to noticeably reduced requirements for large and diverse datasets, which are often used for visual encoder pretraining. Consequently, it addresses a key bottleneck in deploying imitation learning scenarios into practical robotics fields where such vast datasets may not be readily available or feasible to collect.

Theoretically, DynaMo provides evidence supporting a shift in focus towards dynamics-centered self-supervision in learning visual representations for robotics. This suggests an approach more akin to biological analogs found in neuroscience, where internal dynamics models aid control and planning.

Future advancements could explore extending DynaMo's methodology to more complex and less constrained real-world settings beyond laboratory environments. An aspect of interest may involve integrating DynaMo with more sophisticated neural architectures that could further expand capabilities and limitations towards general-adaptative control tasks. Moreover, expanding datasets with unlabeled data might enhance generalization properties, thus broadening the application scope of this pretraining strategy.

In summary, DynaMo provides a robust foundation for advancing the field of visuo-motor control through innovative self-supervised pretraining strategies, thereby promising improvements in efficiency and applicability in both academic research and applied robot learning scenarios.