Changhao Wang

Researcher @ Meta FAIR, Ph.D. UC Berkeley

Email: changhaowang [at] meta [dot] com


I’m a researcher at Meta Fundamental AI Research (FAIR) in Menlo Park, where I focus on embodiment AI and robotic manipulation.

I completed my Ph.D. at UC Berkeley in December 2023, advised by Prof. Masayoshi Tomizuka. During my Ph.D. studies, I interned with Google’s Everyday Robots project in the summers of 2020 and 2022.

Outside of research, I’m a marathon runner.


News

  • 04/2025: Paper: DexterityGen: Foundation Controller for Unprecedented Dexterity accepted by Robotics: Science and Systems (RSS) 2025.
  • 07/2024: Paper: Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments accepted by International Journal of Robotics Research (IJRR).
  • 06/2024: Paper: In-Hand Following of Deformable Linear Objects Using Dexterous Fingers with Tactile Sensing accepted by IROS 2024.
  • 01/2024: Paper: Distributed Multi-agent Interaction Generation with Imagined Potential Games accepted by American Control Conference (ACC) 2024.
  • 12/2023: I defended my Ph.D. dissertation. Here is my thesis.
  • 12/2023: Paper: Robot manipulation task learning by leveraging se (3) group invariance and equivariance accpeted by IEEE Robotics and Automation Letters (RA-L).
  • 09/2023: Paper: Efficient Sim-to-real Transfer of Contact-Rich Manipulation Skills with Online Admittance Residual Learning (website) accepted by CoRL 2023.
  • 06/2023: Paper: A coarse-to-fine framework for dual-arm manipulation of deformable linear objects with whole-body obstacle avoidance (video) won the Best Paper Award at the ICRA 2023 Workshop on Representing and Manipulating Deformable Objects.
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  • 01/2023: Paper: A coarse-to-fine framework for dual-arm manipulation of deformable linear objects with whole-body obstacle avoidance accepted by ICRA 2023.
  • 01/2023: Paper: Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning accepted by ICRA 2023.
  • 05/2022: Paper: Safe Online Gain Optimization for Cartesian Space Variable Impedance Control accepted by CASE 2022
  • 02/2022 Paper: Offline-Online Learning of Deformation Model for Cable Manipulation With Graph Neural Networks accepted by IEEE Robotics and Automation Letters (RA-L).
  • 02/2022: Paper: Robotic cable routing with spatial representation accepted by IEEE Robotics and Automation Letters (RA-L).
  • 01/2022: Paper: Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks accepted by ICRA 2022
  • 01/2022: Paper: BPOMP: A Bilevel Path Optimization Formulation for Motion Planning accepted by ACC 2022
  • 06/2021: Paper: Trajectory Splitting: A Distributed Formulation for Collision Avoiding Trajectory Optimization accepted by IROS 2021.
  • 06/2021: Paper: Online Learning of Unknown Dynamics for Model-Based Controllers in Legged Locomotion accepted by IEEE Robotics and Automation Letters (RA-L).
  • 01/2021: Paper: Contact Pose Identification for Peg-in-hole Assembly under Uncertainties accepted by ACC 2020.
  • 06/2019: Paper: Robust Deformation Model Approximation for Robotic Cable Manipulation accepted by IROS 2019.
  • 07/2018: Paper: A Framework for Manipulating Deformable Linear Objects by Coherent Point Drift accepted by IEEE Robotics and Automation Letters (RA-L).

Research

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DexterityGen: Foundation Controller for Unprecedented Dexterity

Zhao-Heng Yin, Changhao Wang, Luis Pineda, Francois Hogan, Krishna Bodduluri, Akash Sharma, Patrick Lancaster, Ishita Prasad, Mrinal Kalakrishnan, Jitendra Malik, Mike Lambeta, Tingfan Wu, Pieter Abbeel, and Mustafa Mukadam

Robotics: Science and Systems (RSS) 2025

[Paper] [Website]

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Geometric Retargeting: A Principled, Ultrafast Neural Hand Retargeting Algorithm

Zhao-Heng Yin, Changhao Wang, Luis Pineda, Krishna Bodduluri, Tingfan Wu, Pieter Abbeel, and Mustafa Mukadam

In Submission

[Paper] [Website]

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Generalizable whole-body global manipulation of deformable linear objects by dual-arm robot in 3-D constrained environments

Mingrui Yu, Kangchen Lv, Changhao Wang, Yongpeng Jiang, Masayoshi Tomizuka, Xiang Li

International Journal of Robotics Research 2024 (IJRR)

[Paper] [Website] [Code]

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In-Hand Following of Deformable Linear Objects Using Dexterous Fingers with Tactile Sensing

Mingrui Yu, Boyuan Liang, Xiang Zhang, Xinghao Zhu, Lingfeng Sun, Changhao Wang, Shiji Song, Xiang Li, and Masayoshi Tomizuka

IROS 2024

[Paper] [Website] [Code]

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Distributed Multi-agent Interaction Generation with Imagined Potential Games

Lingfeng Sun, Pin-Yun Hung, Changhao Wang, Masayoshi Tomizuka, Zhuo Xu

American Control Conference (ACC) 2024

[Paper] [Website] [Code]

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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] [Simulation Code]

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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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A coarse-to-fine framework for dual-arm manipulation of deformable linear objects with whole-body obstacle avoidance

Mingrui Yu, Kangchen Lv, Changhao Wang, Masayoshi Tomizuka, Xiang Li

ICRA 2023

Best Workshop Paper Award

[Paper] [Website] [Presentation]

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Zero-Shot Policy Transfer with Disentangled Task Representation of Meta-Reinforcement Learning

Zheng Wu, Yichen Xie, Wenzhao Lian, Changhao Wang, Yanjiang Guo, Jianyu Chen, Stefan Schaal, Masayoshi Tomizuka

ICRA 2023

[Paper]

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Prim-LAfD: A Framework to Learn and Adapt Primitive-Based Skills from Demonstrations for Insertion Tasks

Zheng Wu, Wenzhao Lian, Changhao Wang, Mengxi Li, Stefan Schaal, Masayoshi Tomizuka

IFAC 2023

[Paper]

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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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Robotic Cable Routing with Spatial Representation

Shiyu Jin, Wenzhao Lian, Changhao Wang, Masayoshi Tomizuka, Stefan Schaal

IEEE Robotics and Automation Letters (RA-L) 2022

[Paper] [Code]

Image description

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]

Image description

BPOMP: A Bilevel Path Optimization Formulation for Motion Planning

Changhao Wang, Hsien-Chung Lin, Shiyu Jin, Xinghao Zhu, Liting Sun, Masayoshi Tomizuka

American Control Conference (ACC) 2022

[Paper] [Website] [Patent]

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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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Trajectory splitting: A distributed formulation for collision avoiding trajectory optimization

Changhao Wang, Jeffrey Bingham, Masayoshi Tomizuka

IROS 2021

[Paper]

Image description

Contact pose identification for peg-in-hole assembly under uncertainties

Shiyu Jin, Xinghao Zhu, Changhao Wang, Masayoshi Tomizuka

American Control Conference (ACC) 2021

[Paper]

Image description

Robust deformation model approximation for robotic cable manipulation

Shiyu Jin*, Changhao Wang*, Masayoshi Tomizuka

IROS 2019

[Paper] [Website]

Image description

A framework for manipulating deformable linear objects by coherent point drift

Te Tang*, Changhao Wang*, Masayoshi Tomizuka

IEEE Robotics and Automation Letters (RA-L) 2018

[Paper]