ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies
cs.RO, cs.CV
Submitted: 2026-10-03
Updated: 2026-10-09
Code: https://github.com/anthonytao80-crypto/ForeAct3D
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- WorldVLA: Towards Autoregressive Action World Model
- StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
- PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation
- OpenVLA: An Open-Source Vision-Language-Action Model
- QDepth-VLA: Quantized Depth Prediction as Auxiliary Supervision for Vision-Language-Action Models
- Lift3D-VLA: Lifting VLA Models to 3D Geometry and Dynamics-Aware Manipulation
- FASTer: Toward Efficient Autoregressive Vision Language Action Modeling via Neural Action Tokenization
- Decoupled Weight Decay Regularization
- AVID: Adapting Video Diffusion Models to World Models
- VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model
- MVISTA-4D: View-Consistent 4D World Model with Test-Time Action Inference for Robotic Manipulation
- Learning Real-World Action-Video Dynamics with Heterogeneous Masked Autoregression
- ProgressVLA: Progress-Guided Diffusion Policy for Vision-Language Robotic Manipulation
- Learning Interactive Real-World Simulators
- ST4VLA: Spatially Guided Training for Vision-Language-Action Models
- 3D-VLA: A 3D Vision-Language-Action Generative World Model
- FLARE: Robot Learning with Implicit World Modeling
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies