At the Algorithmic Robotics and Control Lab (ARC-L), we are broadly interested in understanding the optimality structures induced by practical problems in robotics/control, and exploiting the gained insight to develop efficient algorithms for tackling these problems with provable guarantees, e.g., on solution optimality. The tools we use/develop include classical combinatorial algorithms and data-driven AI/ML methods. Currently, research at ARC-L explores domains spanning multi-robot path/motion planning, object rearrangement/manipulation, multi-sensor deployment, and sensor fusion.
05/2022 - Dr. Yu will serve as an associated editor for IEEE Transactions on Automation Science and Engineering.
04/2022 - One paper accepted by RSS 2022.
03/2022 - Dr. Yu gave an invited talk at the Rutgers ECE Efficient AI (EFAI) seminar series.
03/2022 - One paper accepted by ICAPS 2022.
02/2022 - Six papers accepted by ICRA 2022.
10/2021 - Our work on Visual Foresight Trees will appear in RA-L.
10/2021 - Our work extending our WAFR 2020 Rubik Table results will appear in IJRR.
09/2021 - Dr. Yu will be starting a 4-year NSF National Robotics Initiative 3.0 project with his colleagues Abdeslam Boularias and Mridul Aanjaneya, working on enabling mobile robots to carry out sophisticated search-n-rescue efforts in degraded environments.
09/2021 - Dr. Yu is serving as an associated editor for IEEE Robotics and Automation Letters.
07/2021 - Dr. Yu is a tenured Associate Professor at Rutgers CS as of 07/2021.
07/2021 - One paper accepted by IROS 2021.
06/2021 - Our AI/ML/CV based metal recycling system paper is accepted by CASE 2021.
We always look to working with motivated prospective students interested in fundamental and/or applied research in robotics. If you are interested in joining the ARC Lab, please read some of our recent papers and let Dr. Yu know why you find them interesting, what are some of the limitations, and how you would go about doing the work better. Generic inquries about “positions” at the lab will not be answered; in particular, saying some of our work is interesting without providing details is a red flag.