Research Focus

Pillar 1: Gripping and Sensing

WP 1: Hybrid soft-grippers with variable stiffness

Asst. Prof. Lum Guo Zhan

  • To create a bio-inspired robotic gripper

 

WP 2: Printed, flexible tactile sensors for robotic grippers

A/P Leong Wei Lin

  • Developing soft sensors that endow grippers with pressure-temperature perception capability

 

Pillar 2: Robotic Arm Manipulation

WP 3: Haptic-based learning for in-hand and dual-arm manipulators

A/P Domenico Campolo

  • Development of a virtual-reality based semi-autonomous framework to train robots to perform both in-hand and dual-arm complex manipulation
  • Human-in-the-loop strategy allows the team to develop techniques that include bio-inspired learning models for motion planning

Pillar 3: Situation awareness, path planning and collision avoidance

WP 4: Cognitive intelligent robotic sensing with AI edge data fusion

A/P Zheng Yuanjin

  • 3D chip-integrated sub-THz radar, optical Lidar, and light-field camera sensor
  • Development of a sub-THz transceiver chip that attains scalable range resolution from cm to mm and AI edge chip for data fusion of multi-sensors for 3D SLAM in real-time

 

WP 5: Multi-level situation awareness for safe and efficient multi-agent collaboration in smart manufacturing

Prof. Xie Lihua

  • New framework considers the mutual intention perception of humans and robots and 3D localization
  • Dynamics are now embraced instead of rejected as a disturbance 

 

WP 6: Resource- and context-aware signal processing and machine learning for real-time decision-making

A/P Andy Khong

  • New metacognitive framework with reduced latency to facilitate fast decision-making,
  • Allows AMRs to re-route, accelerate, decelerate, or detour, instead of hard stopping