From Failures to Learnable Worlds: Policy-Conditioned Environment Generation for Navigation Agents

cs.RO, cs.AI, cs.CV

Submitted: 2026-10-14

Updated: 2026-10-14

Comments: website: https://zhangyuejoslin.github.io/WEDGE/

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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
• 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