85 citations · 181 across the 15 of their papers we have counts for
12 papers · 1 filter
Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning
Xiaohan Zhang, Yifeng Zhu, Yan Ding +4
In existing task and motion planning (TAMP) research, it is a common assumption that experts manually specify the state space for task-level planning. A well-developed state space…
ARDIE: AR, Dialogue, and Eye Gaze Policies for Human-Robot Collaboration
Chelsea Zou, Kishan Chandan, Yan Ding +1
Human-robot collaboration (HRC) has become increasingly relevant in industrial, household, and commercial settings. However, the effectiveness of such collaborations is highly depe…
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Yan Ding, Xiaohan Zhang, Saeid Amiri +5
Task planning systems have been developed to help robots use human knowledge (about actions) to complete long-horizon tasks. Most of them have been developed for "closed worlds" wh…
Grounding Classical Task Planners via Vision-Language Models
Xiaohan Zhang, Yan Ding, Saeid Amiri +4
Classical planning systems have shown great advances in utilizing rule-based human knowledge to compute accurate plans for service robots, but they face challenges due to the stron…
Task and Motion Planning with Large Language Models for Object Rearrangement
Yan Ding, Xiaohan Zhang, Chris Paxton +1
Multi-object rearrangement is a crucial skill for service robots, and commonsense reasoning is frequently needed in this process. However, achieving commonsense arrangements requir…
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…
Robot Task Planning and Situation Handling in Open Worlds
Yan Ding, Xiaohan Zhang, Saeid Amiri +4
Automated task planning algorithms have been developed to help robots complete complex tasks that require multiple actions. Most of those algorithms have been developed for "closed…
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…
ARROCH: Augmented Reality for Robots Collaborating with a Human
Kishan Chandan, Vidisha Kudalkar, Xiang Li +1
Human-robot collaboration frequently requires extensive communication, e.g., using natural language and gestures. Augmented reality (AR) has provided an alternative way of bridging…
Learning to Guide Human Attention on Mobile Telepresence Robots with 360 Vision
Kishan Chandan, Jack Albertson, Xiaohan Zhang +3
Mobile telepresence robots (MTRs) allow people to navigate and interact with a remote environment that is in a place other than the person's true location. Thanks to the recent adv…
Planning Multimodal Exploratory Actions for Online Robot Attribute Learning
Xiaohan Zhang, Jivko Sinapov, Shiqi Zhang
Robots frequently need to perceive object attributes, such as "red," "heavy," and "empty," using multimodal exploratory actions, such as "look," "lift," and "shake." Robot attribut…