RAYA: Learning Where and When to Intervene for Robot Recovery
cs.RO, cs.SY, eess.SY
Submitted: 2026-09-18
Updated: 2026-09-18
Project page: https://raya-control.github.io
Terminology
Sources
- Robust Adaptive Backup Control Barrier Functions
- Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions
- A Control Barrier Function-Constrained Model Predictive Control Framework for Safe Reinforcement Learning
- Cooptimizing Safety and Performance Using Safety Value-Constrained Model Predictive Control
- Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications
- Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement 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