Past/Current Research Projects

Embodied AI uses Vision-Language-Action models to connect perception, language, and action, aiming to build general robots that can adapt, reason, and perform complex tasks in real-world environments.

Distributed reinforcement learning for scalable, decentralized multi-agent path finding in highly-structured environments (e.g., Amazon fulfillment centers)

Communication learning for simultaneous communication and action policy learning.

Deployment of robots to conduct efficient exploration and search in complex environments

Multi-robot search and monitor an area to locate potentially evasive targets, by learning strategies in a mixed cooperative-competitive environment.

Decentralised multi-robot exploration in communication-constraint environments, ensuring appropriate connectivity and adaptability in real-world conditions.

Distributed RL for junction-level traffic light phase control, as well as for decentralized CAVs control via communication learning.

Extension of Model-based Skills Learning to the Multi-Agent Reinforcement Learning Setting.

The project’s aim is to exploit this manipulative prowess in order to boost the performance of legged robots in both industrial and real-world situations.