no code implementations • 17 Mar 2023 • Qiaojie Zheng, Jiucai Zhang, Amy Zhang, Xiaoli Zhang
To address the unreliable and overconfident issues, we introduce a confidence-aware model that predicts uncertainties together with gaze angle estimations.
no code implementations • 26 May 2022 • Lingfeng Tao, Jiucai Zhang, Xiaoli Zhang
Dexterous manipulation tasks usually have multiple objectives, and the priorities of these objectives may vary at different phases of a manipulation task.
no code implementations • 26 May 2022 • Yunsik Jung, Lingfeng Tao, Michael Bowman, Jiucai Zhang, Xiaoli Zhang
In this work, we develop a novel Physics-Guided Deep Reinforcement Learning with a Hierarchical Reward Mechanism to improve learning efficiency and generalizability for learning-based autonomous grasping.
no code implementations • 19 Dec 2020 • Lingfeng Tao, Michael Bowman, Jiucai Zhang, Xiaoli Zhang
In human-robot cooperation, the robot cooperates with humans to accomplish the task together.
no code implementations • 28 Aug 2020 • Fei Ye, Pin Wang, Ching-Yao Chan, Jiucai Zhang
The simulation results shows that the proposed method achieves an overall success rate up to 20% higher than the benchmark model when it is generalized to the new environment of heavy traffic density.
no code implementations • 7 Mar 2020 • Lingfeng Tao, Michael Bowman, Xu Zhou, Jiucai Zhang, Xiaoli Zhang
Enabling robots to provide effective assistance yet still accommodating the operator's commands for telemanipulation of an object is very challenging because robot's assistive action is not always intuitive for human operators and human behaviors and preferences are sometimes ambiguous for the robot to interpret.
no code implementations • 1 Mar 2020 • Lingfeng Tao, Michael Bowman, Jiucai Zhang, Xiaoli Zhang
Applying Deep Reinforcement Learning (DRL) to Human-Robot Cooperation (HRC) in dynamic control problems is promising yet challenging as the robot needs to learn the dynamics of the controlled system and dynamics of the human partner.
no code implementations • 7 Feb 2020 • Fei Ye, Xuxin Cheng, Pin Wang, Ching-Yao Chan, Jiucai Zhang
The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.