Incremental Planning Algorithms for Continuous Non-stationary Environments
2025
Develop incremental planning algorithms designed to operate effectively in non-stationary environments.
Behavior Learning with Reinforcement Learning from Expert Demonstrations
2019
Proposed, developed, and tested deep reinforcement learning algorithms combining expert demonstrations for controlling redundant robot manipulators, research focused on biased exploration algorithms to guide the RL process through explicit exploration demonstrations.
Human Behavior Learning with Imitation Learning
2017
Implemented and analyzed feed-forward neural networks for imitating human behavior in robot manipulator control, contributing to advancements in joint space learning and neural network applications in robotics.