Deep Koopman Operator with Control for Nonlinear Systems

1 code implementation • 16 Feb 2022 • Haojie Shi, Max Q. -H. Meng

Furthermore, most Koopman-based algorithms only consider nonlinear systems with linear control input, resulting in lousy prediction and control performance when the system is fully nonlinear with the control input.

Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal Locomotion

1 code implementation • 14 Sep 2021 • Haojie Shi, Bo Zhou, Hongsheng Zeng, Fan Wang, Yueqiang Dong, Jiangyong Li, Kang Wang, Hao Tian, Max Q. -H. Meng

However, due to the complex nonlinear dynamics in quadrupedal robots and reward sparsity, it is still difficult for RL to learn effective gaits from scratch, especially in challenging tasks such as walking over the balance beam.

reinforcement-learning Reinforcement Learning (RL)

Mobile Robots Autonomous Exploration with Reinforcement Learning

no code implementations • CUHK Course IERG5350 2020 • Haojie Shi, LI ANG

Reinforcement Learning, a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex and uncertain environment.

reinforcement-learning Reinforcement Learning (RL)

INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps

no code implementations • 30 Sep 2019 • Wei Zhan, Liting Sun, Di Wang, Haojie Shi, Aubrey Clausse, Maximilian Naumann, Julius Kummerle, Hendrik Konigshof, Christoph Stiller, Arnaud de La Fortelle, Masayoshi Tomizuka

3) The driving behavior is highly interactive and complex with adversarial and cooperative motions of various traffic participants.

Imitation Learning motion prediction