Project 25: Learning-base Control for Robot Grasping Operation

Contact Information:

Prof. Jianping He

Email: jphe@sjtu.edu.cn

Project Description and Objectives:

 This summer research project focuses on integrating advanced technologies to enhance robotic grasping operations. Here's a concise overview:

1. Incorporating Control Barrier Functions: Develop control strategies using control barrier functions to ensure the safety and stability of robotic grasping in complex environments.

2. Deep Learning Integration: Implement deep learning models to improve the perception and decision-making capabilities of robots during grasping operations, allowing them to adapt to various objects and conditions.

3. Reinforcement Learning for Autonomous Grasping: Utilize reinforcement learning to enable robots to learn optimal grasping strategies through trial and error, enhancing their autonomy and efficiency in real-world applications.

Eligibility Requirements:

Interested students should be proficient in Python/C++ programming, basic knowledge of artificial intelligence and have basic knowledge of robot control. In addition, proficiency in English writing and speaking is mandatory.

Main Tasks:

Develop control strategies using control barrier functions to ensure robotic grasping safety and stability.

Integrate deep learning models to enhance robotic perception and decision-making in grasping operations.

Apply reinforcement learning to enable robots to autonomously learn and optimize grasping techniques. 

Website:

Lab: http://iwin-fins.com

School: http://www.seiee.sjtu.edu.cn/