Control, robotics, planning, and learning
Robotics for safer collaboration, smarter automation, and sustainable manufacturing.
The Control and Robotics Lab at Texas A&M develops planning, control, learning, and prediction methods that help robots work with people in complex real-world environments.
Research themes
We build intelligent robotic systems that reason, adapt, and work around people.
Human-Robot Collaboration
Prediction, planning, and safety-aware control for robots operating near people.
Robotic Disassembly & Recycling
Collaborative robotic systems for end-of-use product disassembly, remanufacturing, and e-waste recovery.
Learning-Based Planning
Neural motion planners, spatial reasoning, and generalist manipulation methods for robot arms.
Control of Dynamic Systems
Robust and learning-enabled control for aerial robots, connected vehicles, and precision systems.
Featured direction
Human-aware robotics for disassembly, recycling, and remanufacturing.
Our work increases the capacity of robotics in labor-intensive disassembly by creating collaborative environments between humans and robots with safety, efficiency, and operational cost in mind.
Recent highlights
Latest from the lab
Now hiring
Ph.D. & postdoc openings
Applications are open for Ph.D. and postdoc positions in the lab — reach out to Prof. Minghui Zheng to apply.
August 2026
Sibo, Davood & Harsh receive Graduate Travel Awards
Congratulations on receiving Texas A&M Graduate and Professional School Research and Presentation Travel Awards.
August 2026
Dr. Zheng co-PI on $24.9M NSF ARM-MIP project
Dr. Zheng will serve as co-PI on the six-year NSF Autonomous Robotic Metallurgist Materials Innovation Platform, contributing robotic solutions for autonomous materials discovery.
July 2026
Sibo, Davood & Jiuzhou win IEEE RAS travel support for IROS 2026
Congratulations on receiving IEEE RAS Travel Support to attend and present at IROS 2026.
July 2026
Jiurun, Dharmik & Chang win Summer/Fall 2026 awards
Congratulations to Jiurun, Dharmik, and Chang on winning fellowships and a research grant recognizing their research progress in the lab.
July 2026
Six new papers at ICML, IROS & ECCV 2026
New work from the group spans foundation-model safety, contact-aware manipulation, motion planning, and human-robot motion prediction.
July 2026
Two papers at MSEC 2026
New work on vision-language-action models and vision-based component segmentation for e-waste disassembly.
July 2026
New work at ACC 2026
Three papers on remanufacturing automation, adaptive human-robot collaboration, and learning-enhanced motion planning.
February 19, 2026
Generalist neural motion planners for robotic manipulators
Our T-ASE survey maps deep learning approaches to planning primitives and identifies challenges in generalization, safety, and real-time deployment.
Opportunities
Interested in robotics research?
We welcome motivated students and researchers with interests in robotics, control, planning, optimization, machine learning, mechatronics, and human-robot collaboration.