LIBERO-VPro: Benchmarking Closed-Loop Visual Robustness of Robotic Foundation Models

arXiv:2609.24350 · cs.RO, cs.CV · Submitted 2026-09-21 · Read on arXiv

cs.RO, cs.CV

Submitted: 2026-09-21

Updated: 2026-09-21

Project page: https://huiqiongli.github.io/LIBERO-VPro

Terminology

• Robustness — 29× in this paper · explained in 5 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Foundation Model — 14× in this paper · explained in 3 episodes
• Foundation Models — 14× in this paper · explained in 7 episodes
• Consistency — 12× in this paper · explained in 3 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Benchmarking — 5× in this paper · explained in 3 episodes
• Success Rate — 5× in this paper
• Affordance — 4× in this paper · explained in 1 episode
• Closed-Loop Control — 4× in this paper · explained in 3 episodes
• Grounding — 4× in this paper · explained in 3 episodes
• Failure Modes — 3× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Semantic Similarity — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-Language-Action (VLA) Models — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
• THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
• LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
• RT-1: Robotics Transformer for Real-World Control at Scale
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• OpenVLA: An Open-Source Vision-Language-Action Model
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• World Action Models are Zero-shot Policies
• Causal World Modeling for Robot Control
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• RLBench: The Robot Learning Benchmark & Learning Environment
• robosuite: A Modular Simulation Framework and Benchmark for Robot Learning

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
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving