Xiang Zhang

Ph.D. Candidate @ UC Berkeley

Email: xiang_zhang_98 [at] berkeley [dot] edu


I’m a fifth-year Ph.D. student in Robotics at the University of California, Berkeley. I work with Prof. Masayoshi Tomizuka in the Mechanical Systems Control (MSC) Laboratory.

My research interest lies in the interdisciplinary combination of robotics, artificial intelligence (AI), and control theories with applications to robot contact-rich manipulation, robot skill learning, and learning for control.


News

  • Oct 2024: our paper “Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task,” is awarded Best Paper in Industrial Robotics Research for Application at IROS 2024
  • Jan 2024: Our paper “Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertion” (website) is accepted by ICRA 2024.
  • Aug 2023: Our paper “Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning” (website) is accepted by CoRL 2023.
  • Aug 2023: I am selected for DSCD Rising Stars Invited Talks at MECC 2023
  • May 2023: I start my Robot learning research internship at the Autodesk Research.
  • Jan 2023: Paper “Learning Generalizable Pivoting Skills” is accepted to ICRA 2023.
  • May 2022: I start my research intern at the Mitsubishi Electric Research Laboratories (MERL).
  • May 2022: Our paper “Safe Online Gain Optimization for Cartesian Space Variable Impedance Control” is accepted by CASE 2022.
  • Feb 2022: Our paper “Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks” is accepted by ICRA 2022.

Research

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Harnessing with Twisting: Single-Arm Deformable Linear Object Manipulation for Industrial Harnessing Task

Xiang Zhang, Hsien-Chung Lin, Yu Zhao, Masayoshi Tomizuka

IROS 2024, Best Paper in Industrial Robotics Research for Application

[Paper] [Video]

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Bridging the Sim-to-Real Gap with Dynamic Compliance Tuning for Industrial Insertion

Xiang Zhang, Masayoshi Tomizuka, Hui Li

ICRA 2024

[Paper] [Website]

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Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning

Xiang Zhang*, Changhao Wang*, Lingfeng Sun, Zheng Wu, Xinghao Zhu, Masayoshi Tomizuka

Conference on Robot Learning (CoRL) 2023

[Paper] [Website]

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Contact-rich SE (3)-Equivariant Robot Manipulation Task Learning via Geometric Impedance Control

Joohwan Seo, Nikhil PS Prakash, Xiang Zhang, Changhao Wang, Jongeun Choi, Masayoshi Tomizuka, Roberto Horowitz

IEEE Robotics and Automation Letters (RAL) 2023

[Paper] [Website] [Code]

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Learning Generalizable Pivoting Skills

Xiang Zhang, Siddarth Jain, Baichuan Huang, Masayoshi Tomizuka, Diego Romeres

ICRA 2023

[Paper]

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Diff-Transfer: Model-based Robotic Manipulation Skill Transfer via Differentiable Simulation

Yuqi Xiang, Feitong Chen, Qinsi Wang, Gang Yang, Xiang Zhang, Xinghao Zhu, Xingyu Liu, Lin Shao

Under Review

[Paper] [Website]

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Safe online gain optimization for Cartesian space variable impedance control

Changhao Wang*, Xiang Zhang*, Zhian Kuang*, Masayoshi Tomizuka

CASE 2022

[Paper] [Website]

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Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks

Changhao Wang, Yuyou Zhang, Xiang Zhang, Zheng Wu, Xinghao Zhu, Shiyu Jin, Te Tang, Masayoshi Tomizuka

IEEE Robotics and Automation Letters (RA-L) 2022

[Paper] [Website] [Code]

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Learning Insertion Primitives with Hybrid Action Space for Robotic Assembly Tasks

Xiang Zhang, Shiyu Jin, Changhao Wang, Xinghao Zhu, Masayoshi Tomizuka

ICRA 2022

[Paper] [Website]

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Online learning of unknown dynamics for model-based controllers in legged locomotion

Yu Sun, Wyatt L Ubellacker, Wen-Loong Ma, Xiang Zhang, Changhao Wang, Noel V Csomay-Shanklin, Masayoshi Tomizuka, Koushil Sreenath, Aaron D Ames

IEEE Robotics and Automation Letters (RA-L) 2021

[Paper] [Video]

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Learning variable impedance control via inverse reinforcement learning for force-related tasks

Xiang Zhang, Liting Sun, Zhian Kuang, Masayoshi Tomizuka

IEEE Robotics and Automation Letters (RA-L) 2021

[Paper] [Website]