Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs
Junhao Shi, Siyin Wang, Xiaopeng Yu, Li Ji, Jingjing Gong, Xipeng Qiu
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Key claim
TAP achieves expert-level performance with minimal labeled data.
This paper presents a new approach to training Vision-Language-Action models that reduces the need for extensive expert data while improving performance. The key result shows that their method can achieve comparable results to models trained on over 1 million expert trajectories using significantly less labeled data.
In plain English
Imagine you're trying to teach a robot how to perform tasks based on visual inputs and instructions. The challenge is that gathering expert demonstrations — the specific actions taken in response to various observations and instructions — is expensive and time-consuming. This often leads to a bottleneck where you can't scale your training effectively because you don't have enough of these expert examples. When you do manage to collect some, the robot might still struggle because it needs to learn not just how to move but also what those movements mean in context. This is where things can go wrong: if the robot focuses too much on the physical actions without understanding the instructions, it won't perform well in real-world scenarios. This is what's called a conflation of learning objectives, where the robot's physical competence and semantic understanding are not aligned properly. The authors propose a solution to this problem by introducing a two-stage training framework called Task-Agnostic Pretraining (TAP). In the first stage, the robot learns from a wide range of unlabeled interactions, including off-task movements and playful actions, which helps it develop a sense of how to move. Then, in the second stage, it grounds this knowledge in language using minimal expert data. This approach allows the robot to build robust physical representations without needing vast amounts of labeled data. The results are promising: on a benchmark, TAP matches the performance of models trained on over a million expert trajectories while using far less labeled data. In practical terms, this means that if you're building an embodied AI system, you can achieve better performance with less reliance on costly expert demonstrations, making your development process more efficient.
The paper introduces a new framework that effectively separates learning objectives in VLA models, which is a significant advancement.
The results are supported by strong benchmarks and demonstrate clear improvements over existing methods.
Deep reliability assessment
The methodology supports the claim that task-agnostic pretraining can reduce the need for labeled data while maintaining performance, but the robustness under different real-world conditions may be overclaimed without broader testing.
Reproducibility
Yes, the paper provides a GitHub repository and links to models on Hugging Face.
Discussion questions
- 1.The authors claim that task-agnostic pretraining can match models trained on over 1M expert trajectories with much less labeled data. How does this claim hold up in environments with different types of noise or unexpected variables?
- 2.The paper uses the SIMPLER benchmark for evaluation. Is this the most appropriate benchmark for testing the robustness of VLA models, or are there other benchmarks that might provide a more comprehensive evaluation?
- 3.Has anyone in the room tried using task-agnostic data for training models in their projects? What challenges did you face, and how did you address them?
- 4.The approach focuses on learning 'how to move' before 'what to do.' Do you think this separation is beneficial in all cases, or are there scenarios where integrating both from the start might be more effective?
- 5.The paper reports a 25% success rate under camera perturbations where other models fail. What specific factors could contribute to this robustness, and how might they be tested further?
Key figure
Figure 1 likely illustrates the two-stage Task-Agnostic Pretraining framework, showing the separation of learning motor priors and grounding them in language.