SOTAVerified

Motion Planning

( Image credit: Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 701–725 of 920 papers

TitleStatusHype
Vandermonde Trajectory Bounds for Linear Companion Systems—0
Vehicle Dynamics Modeling for Autonomous Racing Using Gaussian Processes—0
Verifiably Following Complex Robot Instructions with Foundation Models—0
Vi-LAD: Vision-Language Attention Distillation for Socially-Aware Robot Navigation in Dynamic Environments—0
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios—0
Visualizing High-Dimensional Configuration Spaces: A Comprehensive Analytical Approach—0
VLM See, Robot Do: Human Demo Video to Robot Action Plan via Vision Language Model—0
VLP: Vision Language Planning for Autonomous Driving—0
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users—0
Watch Less, Feel More: Sim-to-Real RL for Generalizable Articulated Object Manipulation via Motion Adaptation and Impedance Control—0
What data do we need for training an AV motion planner?—0
WiseMove: A Framework for Safe Deep Reinforcement Learning for Autonomous Driving—0
With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance—0
WorldPrediction: A Benchmark for High-level World Modeling and Long-horizon Procedural Planning—0
World Robot Challenge 2020 -- Partner Robot: A Data-Driven Approach for Room Tidying with Mobile Manipulator—0
Piecewise linear regressions for approximating distance metrics—0
Plan Explicability and Predictability for Robot Task Planning—0
Planning and Control of Uncertain Cooperative Mobile Manipulator-Endowed Systems under Temporal-Logic Tasks—0
Planning for Complex Non-prehensile Manipulation Among Movable Objects by Interleaving Multi-Agent Pathfinding and Physics-Based Simulation—0
Planning Robot Motion using Deep Visual Prediction—0
Planning under non-rational perception of uncertain spatial costs—0
Planning with Adaptive World Models for Autonomous Driving—0
Planning with affordances: Integrating learned affordance models and symbolic planning—0
Planning with Learned Dynamics: Probabilistic Guarantees on Safety and Reachability via Lipschitz Constants—0
Plan-Seq-Learn: Language Model Guided RL for Solving Long Horizon Robotics Tasks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-DriverCollision0.35—Unverified