Showing 1–20 of 64 results
/ Date/ Name
Jun 10, 2023MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered EnvironmentsFeb 4, 2024Language-guided Active Sensing of Confined, Cluttered Environments via Object Rearrangement PlanningJun 26, 2023SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle DrivingFeb 28, 2017Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-NetworkMar 27, 2017Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environmentsApr 2, 2017Potential Functions based Sampling Heuristic For Optimal Path PlanningFeb 24, 2017Robot gains Social Intelligence through Multimodal Deep Reinforcement LearningApr 14, 2018Intrinsically motivated reinforcement learning for human-robot interaction in the real-worldAug 12, 2020Dynamically Constrained Motion Planning Networks for Non-Holonomic RobotsAug 23, 2022Robot Active Neural Sensing and Planning in Unknown Cluttered EnvironmentsJun 1, 2023Learning Sampling Dictionaries for Efficient and Generalizable Robot Motion Planning with TransformersJul 30, 2023Efficient Q-Learning over Visit Frequency Maps for Multi-agent Exploration of Unknown EnvironmentsMay 26, 2023Multi-Stage Monte Carlo Tree Search for Non-Monotone Object Rearrangement Planning in Narrow Confined EnvironmentsFeb 4, 2025Differentiable Composite Neural Signed Distance Fields for Robot Navigation in Dynamic Indoor EnvironmentsJan 9, 2026Formal Methods in Robot Policy Learning and Verification: A Survey on Current Techniques and Future DirectionsOct 1, 2025Online Hierarchical Policy Learning using Physics Priors for Robot Navigation in Unknown EnvironmentsJul 22, 2018Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered EnvironmentsSep 26, 2018Deeply Informed Neural Sampling for Robot Motion PlanningJun 5, 2021Motion Planning Transformers: A Motion Planning Framework for Mobile RobotsSep 30, 2022NTFields: Neural Time Fields for Physics-Informed Robot Motion Planning