EMC2 Laboratory

Energy Materials Conversion and Combustion

Welcome

Welcome to the EMC2 Lab at UNC Charlotte. We are interested in understanding, designing, and optimizing multi-phase, mutli-scale, thermochemically reacting systems to enable next-generation energy, propulsion, and critical materials technologies. We utilize both experimental and modeling approaches, ranging from lab-scale tests and quantitative diagnostics to computer-assitsted theoretical modeling, to reveal fundmental physcial and chemical mechanisms controlling key process in these systems.

Open Positions

We welcome PhD students, postdoctoral researchers, and visiting students interested in building the next generation of embodied agents, with research directions including robot learning, world models, multimodal agents, data curation, and recursive self-improvement.

Join Us — 3 PhD Openings, Apply by Dec 15

Research Directions

We study how embodied agents can learn from interaction, reason about the physical world, and improve autonomously.

Adaptive Robot Policies

How can robot policies adapt dynamically at inference time to handle new tasks, environments, and embodiment variations?

Interactive World Models

How can structured world models help agents anticipate spatial, temporal, and physical interactions before acting?

Proactive Self-Improvement

How can agents verify and learn from their own predictions to improve continually with minimal human supervision?

Latest News

EMC2 Lab was founded

Daoguan Ning joins the Department of Mechanical Engineering and Engineering Science at the University of North Carolina, Charlotte as an Assistant Professor

Charlotte, 21 Jun. 2026. # Lab Event

Details

Selected Publications

Below is a representative selection of recent publications. For the full publication list, please refer to the Google Scholar profile.

Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment

Jacky Kwok, Xilun Zhang, Mengdi Xu, Yuejiang Liu, Azalia Mirhoseini, Chelsea Finn, Marco Pavone

ECCV, 2026. Best Paper Finalist, CVPR Scalable Robot Learning Workshop

Paper Project Code

RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies

Yinpei Dai, Hongze Fu, Jayjun Lee, Yuejiang Liu, Haoran Zhang, Jianing Yang, Chelsea Finn, Nima Fazeli, Joyce Chai

International Conference on Machine Learning (ICML), 2026. Oral (0.7%)

Paper Project Code

Learning Long-Context Diffusion Policies via Past-Token Prediction

Marcel Torne, Andy Tang, Yuejiang Liu, Chelsea Finn

Conference on Robot Learning (CoRL), 2025. Best Paper, RSS Robot Representation Workshop

Paper Project Code

Demo-SCORE: Curating Demonstrations using Online Experience

Annie Chen, Alec Lessing, Yuejiang Liu, Chelsea Finn

Robotics: Science and Systems (RSS), 2025

Paper Project Code

Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling

Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie, Yoonho Lee, Max Du, Chelsea Finn

International Conference on Learning Representations (ICLR), 2025

Paper Project Code

Contact

Email:

Address: COM 1, 13 Computing Drive, Singapore 117417

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