UniLAP: A Unified Latent Action Policy for Cross-Embodiment Manipulation
cs.RO, cs.CV
Submitted: 2026-10-12
Updated: 2026-10-12
Project page: https://rayyoh.github.io/UniLAP/ABSTRACT
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- Motus: A Unified Latent Action World Model
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- LAOF: Robust Latent Action Learning with Optical Flow Constraints
- Large Video Planner Enables Generalizable Robot Control
- IGOR: Image-GOal Representations are the Atomic Control Units for Foundation Models in Embodied AI
- villa-X: Enhancing Latent Action Modeling in Vision-Language-Action Models
- StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing
- Capturing Visual Environment Structure Correlates with Control Performance
- AdaWorld: Learning Adaptable World Models with Latent Actions
- DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
- World Models for Learning Dexterous Hand-Object Interactions from Human Videos
- World Models
- EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video
- Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- X-IL: Exploring the Design Space of Imitation Learning Policies
- Cross-Hand Latent Representation for Vision-Language-Action Models
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