Yan Ding

I am a forth 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.

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

[Google Scholar (Citation>270)] [CV (May 2023)]

I aim to create a household robot that frees humans from tedious chores, allowing them to enjoy their leisure time. My research focuses on the intersection of planning and learning in complex home environments, using techniques from task and motion planning, machine learning and reinforcement learning.

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.

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.

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

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.

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.

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 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.

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.

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.

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.

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.

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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