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Motion Planning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 920 papers

TitleStatusHype
Path Planning based on 2D Object Bounding-box—0
Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)—0
Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives—0
Path Signatures for Diversity in Probabilistic Trajectory Optimisation—0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
PDE-based multi-agent formation control using flatness and backstepping: analysis, design and robot experiments—0
Pedestrian Prediction by Planning using Deep Neural Networks—0
Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving—0
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing—0
Physics-Guided Hierarchical Reward Mechanism for Learning-Based Robotic Grasping—0
Physics-informed Neural Motion Planning on Constraint Manifolds—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
Policy-Guided Lazy Search with Feedback for Task and Motion Planning—0
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-DriverCollision0.35—Unverified