Action Map Policy: Learning 3D Closed-loop Manipulation via Pixel Classification
The action space poses a major challenge in robot learning, since it is often high-dimensional, can span long time horizons, and frequently admits multi-modal optimal solutions. A good choice of action representation and loss function can help to address these concerns, but there are often trade offs. We propose Action Map Policy (AMP), which casts 3D closed-loop manipulation policy learning as a classification problem in image space. While classification has been an effective formulation in generative language models, applying it to robot action learning is difficult because naively discretizing high-dimensional continuous actions explodes the token vocabulary. Our key idea is to project...
| Authors | Haojie Huang, Zhang Ye, Linfeng Zhao et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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