ActionMap: Robot Policy Learning via Voxel Action Heatmap
Vision-language-action (VLA) models have advanced rapidly across backbones, training recipes, and data scale, yet the action decoder, which converts the backbone's hidden state into a continuous control signal, has barely changed and remains a single-point predictor across the majority of current VLAs. Whether implemented via autoregressive token bins, L1 regression, or flow-matching denoising, the resulting decoder treats the action space as unstructured, leaving the geometric proximity of neighboring actions unexploited during training. To advance this, we introduce ActionMap, a voxel heatmap action head that drops into an existing VLA in place of its native action decoder. For each new...
| Authors | Pei Yang, Hai Ci, Yanzhe Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 5 June 2026 |
| Last updated at source | 10 June 2026 |
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