Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
cs.RO, cs.AI
Submitted: 2026-10-09
Updated: 2026-10-12
Project page: https://afceproject.github.io/AFCE-Web
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- WorldVLA: Towards Autoregressive Action World Model
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
- Unified Visuomotor Targets: Supervising VLAs Beyond Physical Actions
- LARA: Latent Action Representation Alignment for Vision-Language-Action Models
- Being-H0.7: A Latent World-Action Model from Egocentric Videos
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
- FutureVLA: Joint Visuomotor Prediction for Vision-Language-Action Model
- Latent Action Pretraining from Videos
- World Action Models are Zero-shot Policies
- DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
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