PLaCA: Learning Patch-Level Future Latent Change for Action Generation
Vision-Language-Action (VLA) models generate robot actions from visual observations and language instructions, but effective manipulation also requires anticipating how the environment will change.
ProofPaper ↗
Key points
- Existing world-action models explore future image prediction and latent dynamics modeling.
- To address these challenges, we propose PLaCA, a world-to-action framework built around future latent change.
- PLaCA directly predicts spatially structured patch-level future latent change from the current multimodal input, preserving local information about expected environmental changes.
- We introduce patch-level change-aware supervision to emphasize regions undergoing larger changes and a future-to-action interface that explicitly incorporates predicted future latent change into action generation.
Sources (1)
- [1]PLaCA: Learning Patch-Level Future Latent Change for Action GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:15 AM
Vision-Language-Action (VLA) models generate robot actions from visual observations and language instructions, but effective manipulation also requires anticipating how the environment will change.
Existing world-action models explore future image prediction and latent dynamics modeling.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 20, 2026Not All Features Are Created Equal: A Mechanistic Study of Vision-Language-Action Models
- Oct 20, 2026AgenticRoboRL: Bringing Coding-Agent Post-Training to Agentic Robotics
- Oct 19, 2026HAF-VLA: Hierarchical Action Flow Framework for Humanoid Vision-Language-Action Control
- Oct 19, 2026Vision-Language-Delta-Action: Learning Compact Deltas as Actionable Transition Interfaces for Robotic Manipulation
- Oct 18, 2026VLA-Rolling: Fewer Denoising Steps, Better Control via Adaptive Action Reuse
- Oct 18, 2026AffordanceWAM: Affordance-Aware Joint World-Action Modeling for Robot Manipulation