I’m a researcher at Meta Fundamental AI Research (FAIR) in Menlo Park, focusing on Embodied AI and Robotics Manipulation.
I earned my Ph.D. from UC Berkeley in Dec 2023, under the guidance of Prof. Masayoshi Tomizuka. Additionally, I had the opportunity to contribute to the Google’s Everyday Robots project in summer 2020 and 2022.
Outside of work, I am a marathon runner.
News
- 01/2024: Paper: Distributed Multi-agent Interaction Generation with Imagined Potential Games accepted by American Control Conference (ACC) 2024.
- 01/2024: I started as a Research Engineer in the Embodied AI team at Meta FAIR.
- 12/2023: I successfully defended my Ph.D. dissertation and obtained the Ph.D. degree. 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: I begin my Resident at (Google) X, the Moonshot Factory on the Everyday Robots project.
- 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.
- 05/2020: I begin my robotics research intern at (Google) X, the Moonshot Factory.
- 06/2019: Paper: Robust Deformation Model Approximation for Robotic Cable Manipulation accepted by IROS 2019.
- 07/2019: I begin my robotics reserach intern at FANUC Advanced Reserach Laboratory.
- 07/2018: Paper: A Framework for Manipulating Deformable Linear Objects by Coherent Point Drift accepted by IEEE Robotics and Automation Letters (RA-L).
Research
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]
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]
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]
Trajectory splitting: A distributed formulation for collision avoiding trajectory optimization
Changhao Wang, Jeffrey Bingham, Masayoshi Tomizuka
IROS 2021
[Paper]
Contact pose identification for peg-in-hole assembly under uncertainties
Shiyu Jin, Xinghao Zhu, Changhao Wang, Masayoshi Tomizuka
American Control Conference (ACC) 2021
[Paper]
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]