SafeLoop: Risk-Aware Rollback for Vision-Language-Action Manipulation
cs.RO
Submitted: 2026-09-22
Updated: 2026-09-22
Code: https://github.com/Loule0-0/SafeLoop
Terminology
Sources
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- Safe Exploration in Continuous Action Spaces
- RT-1: Robotics Transformer for Real-World Control at Scale
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- WorldVLA: Towards Autoregressive Action World Model
- RynnVLA-002: A Unified Vision-Language-Action and World Model
- SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation
- VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer
- Agentic Robot: A Brain-Inspired Framework for Vision-Language-Action Models in Embodied Agents
- RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation
- Proximal Policy Optimization Algorithms
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
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