Peng Zhou

Imitating tool-based garment folding from a single visual observation using hand-object graph dynamics
Peng Zhou, Jiaming Qi, Anqing Duan, Shengzeng Huo, Zeyu Wu, David Navarro-Alarcon
IEEE Transactions on Industrial Informatics (T-II), 2024
ESI Highly Cited
*, ^†: equal contribution, corresponding author
| arXiv |

In this article, we propose a novel method of learning from demonstrations that enables robots to autonomously manipulate an assistive tool to fold garments. In contrast to traditional methods (that rely on low-level pixel features), our proposed solution uses a dense visual descriptor to encode the demonstration into a high-level hand-object graph (HoG) that allows to efficiently represent the interactions between the manipulated tool and robots.

Lasesom: A latent and semantic representation framework for soft object manipulation
Peng Zhou, Jihong Zhu, Shengzeng Huo, David Navarro-Alarcon
IEEE Robotics and Automation Letters (RA-L), 2021
*, ^†: equal contribution, corresponding author
| arXiv | project page |

In this letter, we present LaSeSOM, a new feedback latent representation framework for semantic soft object manipulation. Our new method introduces internal latent representation layers between low-level geometric feature extraction and high-level semantic shape analysis.

Model predictive manipulation of compliant objects with multi-objective optimizer and adversarial network for occlusion compensation
Peng Zhou, Pai Zheng, Jiaming Qi^†, Chengxi Li, Hoi-Yin Lee, Anqing Duan, Liang Lu, Zhongxuan Li, Luyin Hu, David Navarro-Alarcon
Robotics and Computer-Integrated Manufacturing (RCIM), 2024
*, ^†: equal contribution, corresponding author
| arXiv |

In this paper, we propose a new vision-based controller to automatically regulate the shape of compliant objects with robotic arms. Our method uses an efficient online surface/curve fitting algorithm that quantifies the object's geometry with a compact vector of features; This feedback-like vector enables to establish an explicit shape servo-loop.

Neural reactive path planning with Riemannian motion policies for robotic silicone sealing.
Peng Zhou, Pai Zheng, Jiaming Qi^†, Chengxi Li, Hoi-Yin Lee, Anqing Duan, Liang Lu, Zhongxuan Li, Luyin Hu, David Navarro-Alarcon
Robotics and Computer-Integrated Manufacturing (RCIM), 2022
*, ^†: equal contribution, corresponding author
| arXiv | project page |

In this paper, we present the development of a new method to automate silicone sealing with robotic manipulators. To this end, we propose a novel neural path planning framework that leverages fractional-order differentiation for robust seam detection with vision and a Riemannian motion policy for effectively learning the manipulation of a sealing gun.

Path planning with automatic seam extraction over point cloud models for robotic arc welding
Peng Zhou, Pai Zheng, Jiaming Qi^†, Chengxi Li, Hoi-Yin Lee, Anqing Duan, Liang Lu, Zhongxuan Li, Luyin Hu, David Navarro-Alarcon
IEEE Robotics and Automation Letters (RA-L), 2021
*, ^†: equal contribution, corresponding author
| arXiv | project page |

We present a sim-to-real learning-based approach for real-world humanoid locomotion. To the best of our knowledge, this is the first demonstration of a fully learning-based method for real-world full-sized humanoid locomotion.