HAMSTER: Hierarchical Action Models For Open-World Robot Manipulation
On the map: Vision-language-action models → Generalist VLA models and benchmarks
Abstract (arXiv)
Large foundation models have shown strong open-world generalization to complex problems in vision and language, but similar levels of generalization have yet to be achieved in robotics. One fundamental challenge is the lack of robotic data, which are typically obtained through expensive on-robot operation. A promising remedy is to leverage cheaper, off-domain data such as action-free videos, hand-drawn sketches or simulation data. In this work, we posit that hierarchical vision-language-action (VLA) models can be more effective in utilizing off-domain data than standard monolithic VLA models that directly finetune vision-language models (VLMs) to predict actions. In particular, we study a class of hierarchical VLA models, where the high-level VLM is finetuned to produce a coarse 2D path indicating the desired robot end-effector trajectory given an RGB image and a task description. The intermediate 2D path prediction is then served as guidance to the low-level, 3D-aware control policy capable of precise manipulation. Doing so alleviates the high-level VLM from fine-grained action prediction, while reducing the low-level policy's burden on complex task-level reasoning. We show that, with the hierarchical design, the high-level VLM can transfer across significant domain gaps between the off-domain finetuning data and real-robot testing scenarios, including differences on embodiments, dynamics, visual appearances and task semantics, etc. In the real-robot experiments, we observe an average of 20% improvement in success rate across seven different axes of generalization over OpenVLA, representing a 50% relative gain. Visual results, code, and dataset are provided at: https://hamster-robot.github.io/
Key facts. Cited by 125 papers in the map (data through 7 Oct 2026). Placed in Generalist VLA models and benchmarks, one of 13 directions in Vision-language-action models. Builds on 71 of its 84 references that are in the map. First posted on arXiv 8 Feb 2025.
Builds on
71 of 84 references.
Closest ancestors · 3
Same direction, newest first
- 2024π0: A Vision-Language-Action Flow Model for General Robot Control31 Oct
- 2024OpenVLA: An Open-Source Vision-Language-Action Model13 Jun
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control28 Jul
Other references · 68
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Citing papers per year: 2025: 55; 2026: 70.
125 papers.
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