Projects
Quality-Diversity Reinforcement Learning for Damage Recovery in Robotics
A QD reinforcement learning-based research to enable robots to recover from unexpected mechanical damages in a handful of minutes.
Bidding Hub
A microservice-based real-time auction platform to browse live auctions, place competitive bids, and manage the listings.
Driving Condition-based Energy Management Strategy of Hybrid Vehicles
Developed an online driving condition recognition algorithm using Time Convolutional Networks (TCN) to accurately classify real-time driving conditions. Designed a condition-adaptive energy management strategy, demonstrated lower overall energy consumption across varying driving scenarios.
Variational Autoencoder-based Driving Condition Generation
Developed a driving cycle generation algorithm using a Variational Autoencoder (VAE) to create a representative driving condition database, enabling a lower-cost alternative to real-world data collection while maintaining accuracy and diversity in driving conditions.