Vision-Driven Robotic Manipulation Research Associate

Nanyang Technological University

Singapore

On-site

SGD 60,000 - 100,000

Full time

2 days ago
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Job summary

NTU Singapore's School of Electrical and Electronic Engineering invites applications for a researcher to advance perception and embodied intelligence. The role focuses on building robotic foundation models for general mobile manipulation, leveraging vision-language-action concepts and cutting-edge learning methods.

You will work on efficient manipulation learning from instructional videos and deploy a mobile bimanual manipulation system for household and manufacturing contexts.

Qualifications

  • Master’s in Electrical and Electronic Engineering, Computer Science or related field.
  • 1-year research experience in related area.
  • Expertise in robotic manipulation systems.
  • Knowledge in robotic foundation models.
  • Publication track record is an advantage.
  • Proficiency in basics of programming languages such as ROS and Python.
  • Excellent communication skills, written and verbal – able to communicate complex concepts clearly and accurately.

Responsibilities

  • Develop robotic foundation models for general mobile manipulation based on vision-language-action models.
  • Develop efficient manipulation learning framework from human instructional videos and reduce the data demand for policy learning.
  • Develop a mobile bimanual manipulation system to complete diverse tasks in household environment and in manufacturing deployment scenarios.

Skills

Robotics
ROS
Python
Perception

Education

Master's in Electrical and Electronic Engineering

Tools

ROS
Python

Job description

NTU Singapore's School of Electrical and Electronic Engineering invites applications for a researcher to advance perception and embodied intelligence. The role focuses on building robotic foundation models for general mobile manipulation, leveraging vision-language-action concepts and cutting-edge learning methods.

You will work on efficient manipulation learning from instructional videos and deploy a mobile bimanual manipulation system for household and manufacturing contexts.

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