BarrierFormer: Transformer-Guided Predictive Barrier Enforcement for Safe Robot Control

arXiv:2609.23896 · cs.RO, cs.SY, eess.SY · Submitted 2026-09-20 · Read on arXiv

cs.RO, cs.SY, eess.SY

Submitted: 2026-09-20

Updated: 2026-09-20

Project page: https://safe-robotics-group-at-asu.github.io/BarrierFormer-website

Terminology

• Transformer — 44× in this paper · explained in 2 episodes
• Actor — 36× in this paper · explained in 3 episodes
• Model Predictive Control — 10× in this paper
• Reinforcement Learning — 9× in this paper · explained in 4 episodes
• Latent Representation — 5× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• IEEE Conference on Decision and Control — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Distribution Shift — 3× in this paper · explained in 6 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Zero-Shot Generalization — 3× in this paper · explained in 5 episodes
• Ablation study — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Control Barrier Functions (CBFs) — 2× in this paper · explained in 4 episodes
• European Control Conference — 2× in this paper
• Hybrid System — 2× in this paper · explained in 2 episodes
• Model Predictive Control (MPC) — 2× in this paper · explained in 5 episodes
• Action Space — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• IEEE International Conference on Robotics and Automation — 1× in this paper
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Lyapunov function — 1× in this paper · explained in 2 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• PID Controller — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• World Action Models are Zero-shot Policies
• CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
• Proximal Policy Optimization Algorithms

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