2 citations · 4 across the 16 of their papers we have counts for
8 papers · 1 filter
Incorporating Target Vehicle Trajectories Predicted by Deep Learning Into Model Predictive Controlled Vehicles
Ni Dang, Zengjie Zhang, Jizheng Liu +2
Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a fin…
Identifying Reaction-Aware Driving Styles of Stochastic Model Predictive Controlled Vehicles by Inverse Reinforcement Learning
Ni Dang, Tao Shi, Zengjie Zhang +3
The driving style of an Autonomous Vehicle (AV) refers to how it behaves and interacts with other AVs. In a multi-vehicle autonomous driving system, an AV capable of identifying th…
Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning
Zengjie Zhang, Jayden Hong, Amir Soufi Enayati +1
Reinforcement learning (RL) for motion planning of multi-degree-of-freedom robots still suffers from low efficiency in terms of slow training speed and poor generalizability. In th…
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
Lin-Chi Wu, Zengjie Zhang, Sofie Haesaert +2
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction da…
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives
Jayden Hong, Zengjie Zhang, Amir M. Soufi Enayati +1
Finding an efficient way to adapt robot trajectory is a priority to improve overall performance of robots. One approach for trajectory planning is through transferring human-like s…
Facilitating Sim-to-real by Intrinsic Stochasticity of Real-Time Simulation in Reinforcement Learning for Robot Manipulation
Ram Dershan, Amir M. Soufi Enayati, Zengjie Zhang +2
Simulation is essential to reinforcement learning (RL) before implementation in the real world, especially for safety-critical applications like robot manipulation. Conventionally,…
Sample-Efficient Reinforcement Learning with Symmetry-Guided Demonstrations for Robotic Manipulation
Amir M. Soufi Enayati, Zengjie Zhang, Kashish Gupta +1
Reinforcement learning (RL) suffers from low sample efficiency, particularly in high-dimensional continuous state-action spaces of complex robotic manipulation tasks. RL performanc…
Disturbance Estimation for High-Degree-of-Freedom Euler-Lagrangian Systems Using Sliding Mode Observer without Matching Conditions
Zengjie Zhang, Dirk Wollherr
This paper proposes a novel observer-based disturbance estimation method for high degree-of-freedom Euler-Lagrangian systems using an unknown input-output (UIO) sliding mode observ…