I am Teng Xue, a final-year PhD student at Ecole Polytechnique Federale de Lausanne (EPFL) and a research assistant at Robot Learning and Interaction Group (RLI), Idiap Research Institute, supervised by Dr. Sylvain Calinon. Before joining Idiap/EPFL, I was fortunate to work as a visiting student at ETH RSL, supervised by Dr. David Hoeller, Dr. Martin Wermelinger and Prof. Marco Hutter, and Stanford Artificial Intelligence Lab (SAIL), supervised by Dr. Shenli Yuan and Prof. J. Kenneth Salisbury. I did my internships in Flexiv Robotics (2021).
I am broadly interested in planning and control for robotic systems that can physically interact with the surroundings, especially in task and motion planning and contact-rich manipulation. I am deeply interested in the underlying scientific question: How to cope with the combinatorial complexity arising from the interplay of task (symbolic) and motion (continuous) variables? To tackle this, I have investigated different tools from symbolic AI, tensor factorization, and optimal control. The proposed methods have been validated through several long-horizon dexterous manipulation tasks. Moreover, I am also interested in addressing this challenge from the learning perspective, such as learning from demonstration.
Recent News
New (2025/10) Our paper Sampling-Based Constrained Motion Planning with Product of Experts is accepted to International Journal of Robotics Research (IJRR)!
New (2025/10) Our paper Learning Problem Decomposition for Efficient Sequential Multi-object Manipulation Planning is accepted to IEEE Robotics and Automation Letters (RA-L)!
New (2025/04) Our paper Robust Contact-rich Manipulation through Implicit Motor Adaptation is accepted to International Journal of Robotics Research (IJRR)!
(2024/09) Our paper Robust Manipulation Primitive Learning via Domain Contraction is accepted to Conference on Robot Learning (CoRL), 2024!
(2024/08) Our paper Design and Control of Roller Grasper V3 for In-Hand Manipulation is accepted to IEEE Transactions on Robotics (T-RO)!
(2024/06) Our paper Logic-Geometric Planning and Control Using Graph of Tensor Networks is accepted to RSS24 workshop: Frontiers of optimization for robotics !
(2024/06) Our paper Logic Learning from Demonstrations for Multi-step Manipulation Tasks in Dynamic Environments is accepted to RA-L!
(2024/05) Our paper Logic-Skill Programming: An Optimization-based Approach to Sequential Skill Planning is accepted to Robotics: Science and Systems (RSS), 2024!
(2024/02) I was fortunate to visit TU Berlin and give a talk at the Learning and Intelligent Systems research lab, led by Prof. Marc Toussaint .
(2024/01) Our paper D-LGP: Dynamic Logic-Geometric Program for Combined Task and Motion Planning is accepted to ICRA 2024!
(2024/01) Our paper Generalized Policy Iteration using Tensor Approximation for Hybrid Control is accepted to ICLR 2024 as spotlight (Top 5%)!
(2023/01) Our paper Demonstration-guided Optimal Control for Long-term Non-prehensile Planar Manipulation is accepted to ICRA 2023!
(2022/01) One paper about Robot Grasping in Dense Clutter is accepted to Robotics and Autonomous Systems!
(2020/05) One paper about Multimodel Fusion in pHRI is accepted to IEEE sensors journal!
(2020/02) One paper about Sequential Robot Manipulation is accepted to IEEE Access!
(2019/05) One paper about Tactile Grasping is accepted to ViTac workshop in ICRA 2019!
(2018/05) Our team, Kaibot, won the First Place in Tidy Up My Room Challenge in ICRA 2018!
(2017/02) We won the Outstanding Winner (1/8085) of MCM/ICM competition held by American Consortium for Mathematics and Its Application (COMAP). A nice collaboration with Yasheng and Taihang!