Red-Teaming of Vision-Language-Action Models via Quality Diversity Environment Generation
cs.RO
Submitted: 2026-10-10
Updated: 2026-10-10
Project page: https://qd-env.github.io/qdeg
Sources
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- MolmoAct: Action Reasoning Models that can Reason in Space
- MolmoAct2: Action Reasoning Models for Real-world Deployment
- A Taxonomy for Evaluating Generalist Robot Manipulation Policies
- LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
- What Can RL Bring to VLA Generalization? An Empirical Study
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- ConRFT: A Reinforced Fine-tuning Method for VLA Models via Consistency Policy
- Predictive Red Teaming: Breaking Policies Without Breaking Robots
- Evaluating Gemini Robotics Policies in a Veo World Simulator
- Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control
- Illuminating search spaces by mapping elites
- Large Language Models as In-context AI Generators for Quality-Diversity
- The CMA Evolution Strategy: A Tutorial
- Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
- Scaling Relationship on Learning Mathematical Reasoning with Large Language Models
- DINOv2: Learning Robust Visual Features without Supervision
- Robot Learning as an Empirical Science: Best Practices for Policy Evaluation
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- 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