DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space
cs.RO, cs.AI
Submitted: 2026-09-02
Updated: 2026-09-02
Terminology
Sources
- ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
- End to End Learning for Self-Driving Cars
- VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
- Tree-structured Policy Planning with Learned Behavior Models
- PLUTO: Pushing the Limit of Imitation Learning-based Planning for Autonomous Driving
- When Planners Meet Reality: How Learned, Reactive Traffic Agents Shift nuPlan Benchmarks
- Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?
- Classifier-Free Diffusion Guidance
- Planning by Simulation: Motion Planning with Learning-based Parallel Scenario Prediction for Autonomous Driving
- PlanT: Explainable Planning Transformers via Object-Level Representations
- Score-Based Generative Modeling through Stochastic Differential Equations
- Large Trajectory Models are Scalable Motion Predictors and Planners
- HYPE: Hybrid Planning with Ego Proposal-Conditioned Predictions
- Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
- Guided Conditional Diffusion for Controllable Traffic Simulation
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