Taming an End-to-End Autonomous Driving Policy for Urban Navigation of Quadruped Robots
cs.RO, cs.AI
Submitted: 2026-09-23
Updated: 2026-09-23
Comments: Accepted to IROS 2026 Workshop on AI Meets Autonomy
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- AINav: Large Language Model-Based Adaptive Interactive Navigation
- SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving
- Towards bridging the gap: Systematic sim-to-real transfer for diverse legged robots
- ViNT: A Foundation Model for Visual Navigation
- NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration
- Embodied BERT: A Transformer Model for Embodied, Language-guided Visual Task Completion
- Embodied Navigation Foundation Model
- DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving
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