FeasibleFlow: One-Step Joint Transport of Configuration Feasibility and Trajectories for End-to-End Driving

arXiv:2609.23488 · cs.RO · Submitted 2026-09-20 · Read on arXiv

cs.RO

Submitted: 2026-09-20

Updated: 2026-09-20

Terminology

• Ablation — 5× in this paper · explained in 1 episode
• Flow Matching — 5× in this paper · explained in 8 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Representation Learning — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Online Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

Sources

• Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation
• Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
• DiffusionDriveV2: Reinforcement Learning-Constrained Truncated Diffusion Modeling in End-to-End Autonomous Driving
• Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving

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• 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