29 citations · 30 across the 5 of their papers we have counts for
5 papers · 1 filter
GLAD: Grounded Layered Autonomous Driving for Complex Service Tasks
Yan Ding, Cheng Cui, Xiaohan Zhang +1
Given the current point-to-point navigation capabilities of autonomous vehicles, researchers are looking into complex service requests that require the vehicles to visit multiple p…
Visually Grounded Task and Motion Planning for Mobile Manipulation
Xiaohan Zhang, Yifeng Zhu, Yan Ding +3
Task and motion planning (TAMP) algorithms aim to help robots achieve task-level goals, while maintaining motion-level feasibility. This paper focuses on TAMP domains that involve…
Learning to Ground Objects for Robot Task and Motion Planning
Yan Ding, Xiaohan Zhang, Xingyue Zhan +1
Task and motion planning (TAMP) algorithms have been developed to help robots plan behaviors in discrete and continuous spaces. Robots face complex real-world scenarios, where it i…
Task-Motion Planning for Safe and Efficient Urban Driving
Yan Ding, Xiaohan Zhang, Xingyue Zhan +1
Autonomous vehicles need to plan at the task level to compute a sequence of symbolic actions, such as merging left and turning right, to fulfill people's service requests, where ef…
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Zirui Zhao, Yijun Mao, Yan Ding +2
Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoni…