Heng (Alfredo) Zhang
heng.zhang@iit.it

I am a third-year PhD student in Robotics and Intelligent Machines (DRIM) at Italian Institute of Technology (IIT), advised by Dr. Arash Ajoudani. Previously, I received a Master's degree in Control Engineering from Tongji University and a B.E degree in Automation from Northeast Electric Power University, China.

CV / Google Scholar / LinkedIn / ResearchGate / Twitter / Github

Research Interests

"Contact is the heart of robotic manipulation. To understand manipulation, you must understand contact."

My research focuses on robot learning, reinforcement learning. Specifically, My current research interest focuses on: I would be happy you want to discuss my research with me. I’m open to collaborations, please feel free to send me Email!

Career Goals

Short-term:

  • Complete PhD
  • Apply for a postdoc

Long-term:

  • To be a professor or:
  • Co-found a robotics startup

Outreach

Inspired by Shuijing Liu: For junior PhD, Master's, and undergraduate students as well as potential collaborators, I offer a 30-minute mentorship session. I am especially available to support students from underrepresented groups or those in need. Topics include, but are not limited to, AI, robotics, AI4Sci research, graduate school applications, career development, and life advice. If you'd like to chat, please fill out this form to schedule a meeting.

Note: I do check my email every weekday and respond promptly. Please feel free to send a follow-up email if you haven't received a reply.


News

Research

SRL-VIC Animation SRL-VIC: A variable stiffness-based safe reinforcement learning for contact-rich robotic tasks

Heng Zhang, Gokhan Solak, Gustavo JG Lahr, Arash Ajoudani
IEEE Robotics and Automation Letters (RA-L), 2024
Paper  •   Video  •  


Exploration Policy with Safety and Generalization in contact-rich tasks using Safe Reinforcement Learning and VIC.

passiveRL Animation Towards Passive Safe Reinforcement Learning: A Comparative Study on Contact-rich Robotic Manipulation

Heng Zhang, Gokhan Solak, Sebastian Hjorth, Arash Ajoudani
IEEE Robotics and Automation Letters (RA-L) under review
Paper  •   Hugging Face  •  


Learning to be safe and stable both in training and deployment in real world.

Bresa Bresa: Bio-inspired Reflexive Safe Reinforcement Learning for Contact-Rich Robotic Tasks

Heng Zhang*, Gokhan Solak*, Arash Ajoudani， * equal contribution,
IEEE Robotics and Automation Letters (RA-L) under review
Paper  •   Website  •   Video  •  


A Bio-inspired Reflexive Hierarchical Safe RL method inspired by biological reflexes operating at a higher frequency than the task solver.




AGS Scaling Laws in Scientific Discovery with AI and Robot Scientists

Pengsong Zhang*, Heng Zhang*, Huazhe Xu, Renjun Xu, Zhenting Wang, Cong Wang, Animesh Garg, Zhibin Li, Arash Ajoudani, Xinyu Liu, * equal contribution,
Nature Machine Intelligence in submission
Paper  •   Hugging Face  •   Github


Autonomous Generalist Scientist (AGS) combines agentic AI and embodied robotics to automate the entire research lifecycle.

Service