SOTAVerified

Motion Planning

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

Papers

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Showing 751–775 of 920 papers

TitleStatusHype
The Importance of Prior Knowledge in Precise Multimodal Prediction—0
N 2 C : Neural Network Controller Design Using Behavioral Cloning—0
Combinatorics of a Discrete Trajectory Space for Robot Motion Planning—0
Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving—0
State-switching control of the second-order chained form system—0
Enhancing Lattice-based Motion Planning with Introspective Learning and Reasoning—0
On the Convergence of Overlapping Schwarz Decomposition for Nonlinear Optimal Control—0
Chance-Constrained Trajectory Optimization for Safe Exploration and Learning of Nonlinear Systems—0
Stereo Vision for Unmanned Aerial VehicleDetection, Tracking, and Motion Control—0
Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments—0
PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning—0
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
Autonomous task planning and situation awareness in robotic surgery—0
Multi-Resolution A*—0
CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning—0
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks—0
Go Fetch: Mobile Manipulation in Unstructured Environments—0
Moment State Dynamical Systems for Nonlinear Chance-Constrained Motion Planning—0
Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions—0
FormulaZero: Distributionally Robust Online Adaptation via Offline Population SynthesisCode0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Piecewise linear regressions for approximating distance metrics—0
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning—0
Learning Navigation Costs from Demonstration in Partially Observable Environments—0
Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GPT-DriverCollision0.35—Unverified