Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
cs.RO, cs.AI
Submitted: 2026-05-29
Updated: 2026-09-25
Terminology
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- VLANeXt Family: A Systematic Study of VLA Models from Core Recipes to Emerging Paradigms
- MEM: Multi-Scale Embodied Memory for Vision Language Action Models
- An Anatomy of Vision-Language-Action Models: From Modules to Milestones and Challenges
- EVE: A Generator-Verifier System for Generative Policies
- Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Reinforcement Learning
- When to Act, Ask, or Learn: Uncertainty-Aware Policy Steering
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- RynnVLA-002: A Unified Vision-Language-Action and World Model
- FAST: Efficient Action Tokenization for Vision-Language-Action Models
- A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
- World Action Models are Zero-shot Policies
- Weakly Supervised Anomaly Detection: A Survey
- The Importance of Being a Band: Finite-Sample Exact Distribution-Free Prediction Sets for Functional Data
- OpenAI GPT-5 System Card
- Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
- Qwen3-VL Technical Report
- A Survey on Hallucination in Large Vision-Language 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
- 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