Yan Ding

I am a fifth year computer science PhD student at the State University of New York (SUNY) at Binghamton. I am supervised by Associate Professor Shiqi Zhang and supported by grants from the Ford Motor Company. I was supervised by Chao Chen, a full professor, during my master's program. I received my M.S. in Computer Science in 2019, and got my B.S. in Mechanical Engineering in 2016 from Chongqing University, China. Master Thesis is accessible through this Link. I have created and currently manage an open-source robot simulation project called BestMan, featuring a UR5e robotic arm and a Segway base. This is my YouTube channel, featuring several videos centered around robots by searching @yanding1760.

Feel free to contact me at yding25@binghamton.edu.

[Google Scholar (Citation>300)] [CV (May 2023)]

Research Direction

My research primarily focuses on Task and Motion Planning (TAMP), Reinforcement Learning (RL), Large Language Models (LLMs), and Vision-Language Models (VLMs), with a particular emphasis on their applications in the context of mobile manipulators (MoMa).

Publication





Task and Motion Planning with Large Language Models for Object Rearrangement
Yan Ding*, Xiaohan Zhang*, Chris Paxton, Shiqi Zhang
International Conference on Intelligent Robots and Systems (IROS), 2023
[Paper] [Project] [Video] [Code]

LLM-GROP is a method that uses prompting to extract commonsense knowledge about object configurations from a large language model and instantiates them with a task and motion planner, allowing for successful and efficient multi-object rearrangement in various environments using a mobile manipulator.

ARDIE: AR, Dialogue, and Eye Gaze Policies for Human-Robot Collaboration
Chelsea Zou, Kishan Chandan, Yan Ding, Shiqi Zhang
ICRA Workshop on CoPerception: Collaborative Perception and Learning, 2023
[Paper]
Symbolic State Space Optimization for Long Horizon Mobile Manipulation Planning
Xiaohan Zhang, Yifeng Zhu, Yan Ding, Yuqian Jiang, Yuke Zhu, Peter Stone, and Shiqi Zhang
International Conference on Intelligent Robots and Systems (IROS), 2023
[Paper]
Learning to Reason about Contextual Knowledge for Planning under Uncertainty
Cheng Cui, Saeid Amiri, Yan Ding, Xingyue Zhan, Shiqi Zhang
The Conference on Uncertainty in Artificial Intelligence (UAI), 2023
[Paper]
GLAD: Grounded Layered Autonomous Driving for Complex Service Tasks
Yan Ding, Cheng Cui, Xiaohan Zhang, Shiqi Zhang
Under Review
[Paper]

A new planning framework called GLAD has been developed for autonomous urban driving to enable efficient and safe fulfillment of complex service requests.

ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A
Kai Gao, Yan Ding, Shiqi Zhang, Jingjin Yu
Under Review
[Paper]

In this research, we propose ORLA*, which leverages delayed (lazy) evaluation in searching for a high-quality object pick and place sequence that considers both end-effector and mobile robot base travel.

Grounding Classical Task Planners via Vision-Language Models
Xiaohan Zhang, Yan Ding, Saeid Amiri, Hao Yang, Andy Kaminski, Chad Esselink, and Shiqi Zhang
ICRA Workshop on Robot Execution Failures and Failure Management Strategies, 2023
[Paper]
Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds
Yan Ding, Xiaohan Zhang, Saeid Amiri, Nieqing Cao, Hao Yang, Chad Esselink, Shiqi Zhang
Autonomous Robots (accepted)
[Paper] [Project] [Video] [Code]

The paper introduces a new algorithm (COWP) that uses task-oriented common sense extracted from Large Language Models to help robots handle unforeseen situations and complete complex tasks in an open world, with better success rates than previous algorithms.

Learning to Ground Objects for Robot Task and Motion Planning
Yan Ding, Xiaohan Zhang, Xingyue Zhan, Shiqi Zhang
IEEE Robotics and Automation Letters (RA-L), 2022
[Paper] [Project] [Code] [Presentation]

The paper presents a new robot planning algorithm, TMOC, which can handle complex real-world scenarios without prior knowledge of object properties by learning them through a physics engine, outperforming existing algorithms.

Visually Grounded Task and Motion Planning for Mobile Manipulation
Xiaohan Zhang, Yifeng Zhu, Yan Ding, Yuke Zhu, Peter Stone, and Shiqi Zhang
International Conference on Robotics and Automation (ICRA), 2022
[Paper] [Project]
Task and Situation Structures for Case-Based Planning
Hao Yang, Tavan Eftekhar, Chad Esselink, Yan Ding, Shiqi Zhang
International Conference on Case-Based Reasoning (ICCBR), 2021
[Paper]
Task-Motion Planning for Safe and Efficient Urban Driving
Yan Ding, Xiaohan Zhang, Xingyue Zhan, Shiqi Zhang
International Conference on Intelligent Robots and Systems (IROS), 2020.
[Paper] [Project] [Code] [Demo] [Presentation]

Autonomous vehicles need to balance efficiency and safety when planning tasks and motions, and the algorithm Task-Motion Planning for Urban Driving (TMPUD) enables communication between planners for optimal performance.

DAVT: an error-bounded vehicle trajectory data representation and compression framework
Chao Chen*, Yan Ding*, Suiming Guo, Yasha Wang
IEEE TVT, 2020.
[PDF]

DAVT proposes a mobile edge computing solution for vehicle trajectory data compression, which reduces data at the source and lowers communication and storage costs, using three compressors for distance, acceleration, velocity, and time data parts, and outperforms other baselines according to evaluation results.

VTracer: When online vehicle trajectory compression meets mobile edge computing
Chao Chen*, Yan Ding*, Zhu Wang, Junfeng Zhao, Bin Guo, Daqing Zhang
IEEE Systems Journal, 2019.
[PDF]

This paper proposes an online trajectory compression framework that uses SD-Matching for GPS alignment and HCC for compression, and demonstrates its effectiveness and efficiency using real-world datasets in Beijing and deployment in Chongqing.

TrajCompressor: An Online Map-matching-based Trajectory Compression Framework Leveraging Vehicle Heading Direction and Change
Chao Chen*, Yan Ding*, Xuefeng Xie, Shu Zhang, Zhu Wang, Liang Feng
IEEE TITS, 2019.
[PDF]

This paper presents an online trajectory compression framework for reducing storage, communication, and computation issues caused by massive and redundant vehicle trajectory data, consisting of two phases: online trajectory mapping and trajectory compression, using Spatial-Directional Matching and Heading Change Compression algorithms respectively, which have been evaluated with real-world datasets in Beijing and deployed in Chongqing, showing higher accuracy and efficiency compared to state-of-the-art algorithms.

Fuel Consumption Estimation of Potential Driving Paths by Leveraging Online Route APIs
Chao Chen*, Yan Ding*, Xuefeng Xie, Xuefeng Xie, Zhikai Yang
Green, Pervasive, and Cloud Computing: 13th International Conference (GPC), 2018.
[PDF]

This paper proposes a fuel consumption model based on GPS trajectory and OBD-II data, which can estimate the fuel usage of driving paths and help drivers choose fuel-efficient routes to reduce greenhouse gas and pollutant emissions.

A three-stage online map-matching algorithm by fully using vehicle heading direction
Chao Chen*, Yan Ding*, Xuefeng Xie, Shu Zhang
Journal of Ambient Intelligence and Humanized Computing, 2018.
[PDF]

The SD-Matching algorithm proposes a three-stage approach to improve the accuracy and speed of online map-matching by incorporating vehicle heading direction data.

Greenplanner: Planning personalized fuel-efficient driving routes using multi-sourced urban data
Yan Ding*, Chao Chen*, Shu Zhang, Bin Guo, Zhiwen Yu, Yasha Wang
IEEE PerCom, 2017.
[PDF]

Greenhouse gas emissions from vehicles in modern cities is a significant problem, but recommending fuel-efficient routes to drivers through a personalized fuel consumption model can help alleviate this issue, as demonstrated by the successful implementation of GreenPlanner in Beijing, which achieved a mean fuel consumption error of less than 7% and an average savings of 20% fuel consumption for suggested routes.


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