My research goal is to establish a statistical learning control framework by unifying statistical machine learning and control theory, with applications in robotics. In particular, I am interested in statistical machine learning for system identification and control.
- Janurary 15, 2020, Two Postdoc positions openning on “Safe Reinforcement Learning for Motion Planning” funded by AnKobot. One position is on Simulator. One position is on Theory.
- Janurary 14, 2020, Our paper “Towards Lossless Binary Convolutional Neural Networks Using Piecewise Approximation” accepted to ECAI, 2020. Well done, Baozhou.
- October 25, 2019, Our paper “Data Driven Discovery of Cyber Physical Systems” is published in Nature Communications!
- October 22, 2019, Our paper “Asynchronous Observer Design for Switched Linear Systems: A Tube-Based Approach” is published in IEEE/CAA Journal of Automatica Sinica.
- October 2, 2019, Our paper “H∞ Model-free Reinforcement Learning with Robust Stability Guarantee” is accepted to NeurIPS 2019 Workshop on Robot Learning.
- September 12, 2019. Dr Qingrui Zhang from University of Toronto joined our group as a PostDoc, co-supervised by Dr Vasso Reppa from Department of Maritime and Transport Technology. Welcome, Qingrui!
- April 22, 2019. Our paper “BayesNAS: A Bayesian Approach for Neural Architecture Search” accepted to ICML 2019! Hongpeng also got the ICML Travel Award. Well done, Hongpeng and Minghao.
- April 11, 2019. Hongpeng gave a talk on “Sparse Bayesian Deep Neural Networks for Nonlinear System Identification” at Nonlinear System Identification Benchmarks, Eindhoven.
- April 1, 2019. Talk at Huawei Noah’s Ark Lab, London, UK.
- Febrary 11, 2019. One Postdoc position openning on “Learning for Self-healing of Multi-Machine Systems”, apply here
- Febrary 8, 2019. Two abstracts accepted to Benelux Meeting.
- December 21, 2018. Our paper Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning accepted to ICLR 2019!
- May 1, 2018. Start my research group in the Department of Cognitive Robotics, TU Delft. Thanks to the amazing colleagues in DJI! Thanks Shenzhen!