SWAP: Stepwise Action Policy Routing for Vision-Language-Action Models
cs.RO
Submitted: 2026-10-03
Updated: 2026-10-03
Sources
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
- Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey
- AutoMix: Automatically Mixing Language Models
- AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
- Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
- RoboReward: General-Purpose Vision-Language Reward Models for Robotics
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