Profile Photo of Weiyu Liu

Weiyu Liu

I am an incoming Assistant Professor in the Kahlert School of Computing at the University of Utah. Currently, I am a Postdoctoral Scholar working with Jiajun Wu in the CogAI group and Stanford Vision and Learning Lab (SVL). I completed my Ph.D. in robotics at Georgia Institute of Technology, advised by Sonia Chernova. During my Ph.D., I interned at the NVIDIA Seattle Robotics Lab, working with Dieter Fox, Tucker Hermans, Animesh Garg, and Chris Paxton. Prior to joining the Ph.D. program, I received my Bachelor's degree in Electrical Engineering from Georgia Tech.

Email  /  CV (Dec 2024)  /  Google Scholar  /  Github  /  Twitter

Research

The goal of my research is to develop robots that can effectively perceive, model, and interact with the real world. I am particularly interested in the application where human users can easily command robots to complete long-horizon tasks via simple language commands, such as "make the tea". Achieving this requires robust generalization across various dimensions, including objects, environments, actions, and tasks. My core insight is that strong generalization can be achieved through a structured representation of world knowledge. Rather than constructing this representation from scratch, robots should leverage the rich knowledge embedded in human language, grounding it through interactions with their environments to connect abstract knowledge to their sensorimotor capabilities.

My prior research has investigated core components of this structured knowledge representation, methods to extract and refine this knowledge extracted from humans, and algorithms that enable reasoning with this knowledge to support broad generalization.

News
Group

I am looking for motivated and talented students to join my research group.

Thank you for your interest in joining our research group! Please review the information below about our research environment and goals.

If you are a prospective PhD student interested in joining my group, especially for the coming application season, please fill out this PhD Opportunities Form. If you are interested in working with me as an undergraduate student, master's student, postdoc, research intern, or visiting scholar, please complete this Research Opportunities Form. I apologize that I might not be able to respond to all emails, but I will carefully review all submitted forms.


Research Vision

We tackle problems that bring new ideas and make a real-world difference. Our projects can lead to new algorithms, datasets, benchmarks, or integrated hardware and software solutions.

Publishing and Dissemination

I encourage students to publish one paper each year, something you are genuinely proud to share. Alongside the paper, we release code, datasets, project websites, and other materials to help the community build on our work.

Mentorship and Advising

I will schedule recurring meetings with all group members and make it a priority to meet individually with each PhD student every week. We also hold regular lab meetings / reading groups to share ideas and learn from one another. I believe strong research comes from a supportive, honest, and intellectually open environment.

For research, I will support your growth in identifying meaningful questions, developing solutions, writing, sharing work, managing projects, giving and receiving constructive feedback, and shaping a strong research narrative over time.

For your career, I will provide advice on finding internships, building your network, mentoring junior students, and preparing for academic or industry jobs.

For personal growth beyond research, I encourage you to balance work and life, travel (for example, by attending international conferences), and continue developing your interests and hobbies.

Funding and Resources

I will work to secure funding to support PhD students. Students will have access to high-performance compute resources, robot hardware, personal computers, and travel support for attending conferences.

What I'm Looking For

I'm looking for students who are excited in the research directions we pursue. Ideal candidates:

  • Have prior research experience in robot learning, manipulation, task and motion planning, or related fields such as AI, machine learning, computer vision, NLP
  • Think deeply and communicate clearly
  • Have strong coding and hands-on skills
  • Are self-driven
Publications
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Years: 2024 / 2023 / 2022 / 2021 / 2020 / 2019 / Before 2018
Learning Compositional Behaviors from Demonstration and Language

Weiyu Liu*, Neil Nie*, Ruohan Zhang, Jiayuan Mao, Jiajun Wu
Conference on Robot Learning (CoRL), 2024
2nd Workshop on Learning Effective Abstractions for Planning (LEAP), 2024 (Oral Presentation)
paper / website / bibtex

Composable Part-Based Manipulation

Weiyu Liu, Jiayuan Mao, Joy Hsu, Tucker Hermans, Animesh Garg, Jiajun Wu
Conference on Robot Learning (CoRL), 2023
arxiv / website / bibtex

Planning abstractions figure
Learning Planning Abstractions from Language

Weiyu Liu*, Geng Chen*, Joy Hsu, Jiayuan Mao, Jiajun Wu
International Conference on Learning Representations (ICLR), 2024
arxiv / website / bibtex

IKEA Manuals at Work: 4D Grounding of Assembly Instructions on Internet Videos

Yunong Liu, Cristobal Eyzaguirre, Manling Li, Shubh Khanna, Juan Carlos Niebles, Vineeth Ravi, Saumitra Mishra, Weiyu Liu, Jiajun Wu
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2024
paper / website / code and data / bibtex

3D visual grounding figure
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners

Chun Feng*, Joy Hsu*, Weiyu Liu, Jiajun Wu
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
paper / website / code / bibtex

Embodied agent interface figure
Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making

Manling Li*, Shiyu Zhao*, Qineng Wang*, Kangrui Wang*, Yu Zhou*, Sanjana Srivastava, Cem Gokmen, Tony Lee, Li Erran Li, Ruohan Zhang, Weiyu Liu, Percy Liang, Li Fei-Fei, Jiayuan Mao, Jiajun Wu
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2024 (Oral Presentation)
paper / website / code / bibtex

MARPLE benchmark figure
MARPLE: A Benchmark for Long-Horizon Inference

Emily Jin, Zhuoyi Huang, Jan-Philipp Fränken, Weiyu Liu, Hannah Cha, Sarah A. Wu, Erik Brockbank, Ruohan Zhang, Jiajun Wu, Tobias Gerstenberg
Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, 2024
arxiv / website / code and data / bibtex

Survey paper thumbnail
A Survey of Semantic Reasoning Frameworks for Robotic Systems

Weiyu Liu1, Angel Daruna1, Maithili Patel2, Kartik Ramachandruni2, Sonia Chernova
Robotics and Autonomous Systems, 2023
paper / bibtex

StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects

Weiyu Liu, Yilun Du, Tucker Hermans, Sonia Chernova, Chris Paxton
Robotics: Science and Systems (RSS), 2023
CoRL Workshop on Language and Robot Learning, 2022
arxiv / code & data / website / bibtex

Latent Space Planning for Multi-Object Manipulation with Environment-Aware Relational Classifiers

Yixuan Huang, Nichols Crawford Taylor, Adam Conkey, Weiyu Liu, Tucker Hermans
Transactions on Robotics (TR-O), 2023
arxiv / website / bibtex

GraspGPT figure
GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping

Chao Tang, Dehao Huang Wenqi Ge, Weiyu Liu, Hong Zhang
Robotics and Automation Letters (RA-L), 2023
arxiv / code & data / website / bibtex

StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects

Weiyu Liu, Chris Paxton, Tucker Hermans, Dieter Fox
International Conference on Robotics and Automation (ICRA), 2022
arxiv / code & data / website / talk / bibtex

Learning Instance-Level N-Ary Semantic Knowledge At Scale For Robots Operating in Everyday Environments

Weiyu Liu, Dhruva Bansal, Angel Daruna, Sonia Chernova
Robotics: Science and Systems (RSS), 2021
Autonomous Robots, 2023 (Invited Submission)
paper / journal (extended version) / code & data / talk / bibtex

Towards Robust One-shot Task Execution using Knowledge Graph Embeddings

Angel Daruna, Lakshmi Nair, Weiyu Liu, Sonia Chernova
International Conference on Robotics and Automation (ICRA), 2021
arxiv / bibtex

An Affordance Keypoint Detection Network for Robot Manipulation

Ruinian Xu, Fu-Jen Chu, Chao Tang, Weiyu Liu, Patricio Vela
IEEE Robotics and Automation Letters (RA-L), 2021
paper / code & data / bibtex

Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping

Adithya Murali, Weiyu Liu, Kenneth Marino, Sonia Chernova, Abhinav Gupta
Conference on Robot Learning (CoRL), 2020
arxiv / code & data / video / talk / project page / bibtex

CAGE: Context-Aware Grasping Engine

Weiyu Liu, Angel Daruna, Sonia Chernova
International Conference on Robotics and Automation (ICRA), 2020
arxiv / code & data / video / press / bibtex

Path Ranking with Attention to Type Hierarchies

Weiyu Liu, Angel Daruna, Zsolt Kira, Sonia Chernova
Conference on Artificial Intelligence (AAAI), 2020 (Oral Presentation)
arxiv / code / bibtex

RoboCSE: Robot Common Sense Embedding

Angel Daruna, Weiyu Liu, Zsolt Kira, Sonia Chernova
International Conference on Robotics and Automation (ICRA), 2019
arxiv / code & data / video / bibtex

Recovery-Driven Development figure
Taking Recoveries to Task: Recovery-Driven Development for Recipe-based Robot Tasks

Siddhartha Banerjee*, Angel Daruna*, David Kent*, Weiyu Liu*, Jonathan Balloch, Abhinav Jain, Akshay Krishnan, Muhammad Asif Rana, Harish Ravichandar, Binit Shah, Nithin Shrivatsav, Sonia Chernova
International Symposium on Robotics Research (ISRR), 2019
paper / code / bibtex

Autonomous flying blimp interaction with human in an indoor space

Ningshi Yao, Qiuyang Tao, Weiyu Liu, Zhen Liu, Ye Tian, Peiyu Wang, Timothy Li, Fumin Zhang
Frontiers of Information Technology & Electronic Engineering, 2019
paper / bibtex

Situated Robot Knowledge figure
SiRoK: Situated Robot Knowledge - Understanding the Balance Between Situated Knowledge and Variability

Sonia Chernova, Vivian Chu, Angel Daruna, Haley Garrison, Meera Hahn, Priyanka Khante, Weiyu Liu, Andrea Thomaz
AAAI Spring Symposium Series (AAAI-SSS), 2018
paper / bibtex

Situated Bayesian Reasoning Framework for Robots Operating in Diverse Everyday Environments

Sonia Chernova, Vivian Chu, Angel Daruna, Haley Garrison, Meera Hahn, Priyanka Khante, Weiyu Liu, Andrea Thomaz
International Symposium on Robotics Research (ISRR), 2017
paper / bibtex

Mentoring

Service