Projects

Legged Locomotion with Graph Neural Network Policies
Proposing structured control policies for locomotion tasks via graph neural networks based on the robot configuration morphology.
Legged Locomotion with Graph Neural Network Policies
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Proposing a cooperative generative adversarial method for obtaining controllable skill sets from unlabeled datasets containing diverse state transition patterns.
Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations
Proposing a generative adversarial method for inferring reward functions from partial and potentially physically incompatible demonstrations for successful skill acquirement where reference or expert demonstrations are not easily accessible.
Learning Agile Skills via Adversarial Imitation of Rough Partial Demonstrations
Object Manipulation via Hierarchical Reinforcement Learning Control
Employing a hierarchical control method to achieve complex high-level tasks by integrating locomotion and manipulation skills.
Object Manipulation via Hierarchical Reinforcement Learning Control
Autonomous Pose Tracking with Compositional Reinforcement Learning Policies
Employing a novel compositional control structure to allow legged systems to achieve full-pose trajectory tracking in a global coordinate system by integrating learned skills.
Autonomous Pose Tracking with Compositional Reinforcement Learning Policies
Hierarchical Deep Reinforcement Learning for Legged Robot Navigation
Learning to utilize learned low-level skills in high-level tasks by considering only high-level objectives.
Code
Hierarchical Deep Reinforcement Learning for Legged Robot Navigation
Actor-Critic Reinforcement Learning for a Lunar Lander
Extending vanilla policy gradients with advantage estimation.
Code
Actor-Critic Reinforcement Learning for a Lunar Lander
Gaussian Process Regression for Groundwater Pollution Prediction
Predicting model posterior distribution with Gaussian Process regression from scratch, which is then applied to an inference problem based on space data.
Code
Gaussian Process Regression for Groundwater Pollution Prediction
Hyperparameter Tuning with Constrained Bayesian Optimization
Joint training of model and objective with appropriate acquisition function and constraint satisfaction.
Code
Hyperparameter Tuning with Constrained Bayesian Optimization
Predicting Uncertainty with Bayesian Neural Networks on MNIST Dataset
Comparing classification uncertainty prediction with DenseNets and Bayesian Neural Networks.
Code
Predicting Uncertainty with Bayesian Neural Networks on MNIST Dataset