Tony Wang
02/25/2025
The Surprising Effectiveness of Representation Learning for Visual Imitation
https://arxiv.org/pdf/2112.01511
An Unbiased Look at Datasets for Visuo-Motor Pre-Training
https://openreview.net/pdf?id=qVc7NWYTRZ6
In robot learning, visual representation learning has been a hot topic, especially in imitation learning and visuomotor control. Two recent papers—“The Surprising Effectiveness of Representation Learning for Visual Imitation” and “An Unbiased Look at Datasets for Visuo-Motor Pre-Training”—offer interesting perspectives on how well visual representations can transfer to robotic tasks. But do these methods truly generalize? Based on my own testing, I have some reservations.
This paper argues that visual imitation learning struggles because it tries to solve two problems at once:
To address this, the authors propose decoupling representation learning from behavior learning. They pre-train a vision encoder using Bootstrap Your Own Latent (BYOL) and then use a k-nearest neighbor (kNN) approach to predict actions during deployment. Their results suggest that good representations alone can go a long way in enabling imitation, even without end-to-end behavior cloning.
This paper explores how dataset choice impacts visuomotor pre-training. The authors compare several large-scale datasets—including ImageNet, Ego4D, 100 Days of Hands, Kinetics, and RoboNet—and evaluate their effectiveness in fine-tuning for robotic manipulation.
Key takeaways:
This challenges the assumption that robotics-specific data is always better. Instead, broader internet-scale datasets seem to provide stronger priors for robot learning.
In my test on PI0-FAST, it seems the 3B vlm encoder can handle complex vision input easily, the vision prior will not be affected by background change.
Instead of relying purely on end-to-end visual pre-training, object-centric representations might provide a more structured way to improve generalization. Papers like POCR and HODOR propose breaking down scenes into meaningful object representations, making it easier to generalize across different environments. This is especially relevant for multi-step tasks, where pure pixel-based representations often struggle.
Another promising direction is Embodied Chain of Thought (e-CoT), which integrates reasoning into visuomotor learning. Instead of treating imitation as a direct mapping from pixels to actions, e-CoT introduces hierarchical reasoning, helping robots understand task structure rather than just replicating motions.
Both papers provide valuable insights, but I believe future progress will come from integrating structured representations, task priors, and reasoning-based approaches. Instead of simply scaling up datasets or tweaking pre-training methods, we should explore how robots can break down and reason about their environments—something object-centric learning and hierarchical reasoning models like e-CoT are starting to tackle.
Would love to hear thoughts from others—should we continue refining visual representation learning, or is it time to rethink the entire approach?