SOTAVerified

Trajectory Planning

Trajectory planning for industrial robots consists of moving the tool center point from point A to point B while avoiding body collisions over time. Trajectory planning is sometimes referred to as motion planning and erroneously as path planning. Trajectory planning is distinct from path planning in that it is parametrized by time. Essentially trajectory planning encompasses path planning in addition to planning how to move based on velocity, time, and kinematics.

Papers

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Showing 1–25 of 324 papers

TitleStatusHype
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIsCode5
ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View SynthesisCode5
Planning-oriented Autonomous DrivingCode4
OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action ModelCode4
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-TuningCode3
VAD: Vectorized Scene Representation for Efficient Autonomous DrivingCode3
Epona: Autoregressive Diffusion World Model for Autonomous DrivingCode3
GPD-1: Generative Pre-training for DrivingCode2
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning MethodCode2
FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown EnvironmentsCode2
RoCo: Dialectic Multi-Robot Collaboration with Large Language ModelsCode2
Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-TuningCode2
ORFD: A Dataset and Benchmark for Off-Road Freespace DetectionCode2
A Simple and Model-Free Path Filtering Algorithm for Smoothing and AccuracyCode2
Generative Planning with 3D-vision Language Pre-training for End-to-End Autonomous DrivingCode2
Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenesCode2
LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based PlanningCode1
Learning Discrete World Models for Heuristic SearchCode1
Model-aided Federated Reinforcement Learning for Multi-UAV Trajectory Planning in IoT NetworksCode1
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph ConstructionCode1
GNN-Empowered Effective Partial Observation MARL Method for AoI Management in Multi-UAV NetworkCode1
Lane Graph Estimation for Scene Understanding in Urban DrivingCode1
MTP: Multi-Hypothesis Tracking and Prediction for Reduced Error PropagationCode1
Dynamic Neural Potential Field: Online Trajectory Optimization in Presence of Moving ObstaclesCode1
Convex Risk Bounded Continuous-Time Trajectory Planning and Tube Design in Uncertain Nonconvex EnvironmentsCode1
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All datasetsnuScenesToolBench

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-P3 (Lidar)Collision-3s1.27—Unverified
2UniADCollision-3s0.71—Unverified
3VAD-Base [jiang2023vad]Collision-3s0.24—Unverified
4AD-MLPCollision-3s0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT4-TOPGUNWin rate86.54—Unverified
2Attention BucketWin rate71.5—Unverified
3GPT4- DFSDTWin rate70.4—Unverified