Shengzeng Huo (霍盛增)

Ph.D. student at The Hong Kong Polytechnic University , advised by Dr David Navarro Alarcon, Robotics and Machine Intelligence lab. I received my B.Sc. in Automotive engineering from South China University of Technology in 2019.

My research focuses on Bimanual manipulation, Deformable object manipulation, Robot learning, Motion planning.

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To facilitate the efficient learning of robot manipulation skills, in this work, we propose a new approach comprised of three modules: (1) learning of general prior knowledge with random explorations in simulation, including state representations, dynamic models, and the constrained action space of the task; (2) extraction of a state alignment-based reward function from a single demonstration video; (3) real-time optimization of the imitation policy under systematic safety constraints with sampling-based model predictive control

To develop advanced robotic manipulation capabilities in unstructured environments that avoid these assumptions, we propose a novel long-horizon framework that exploits contrastive planning in finding promising collaborative actions.

This letter addresses the problem of contact-based manipulation of deformable linear objects (DLOs) towards desired shapes with a dual-arm robotic system.

We present a novel sensor-based system to perform defect inspection tasks automatically over free-form specular surfaces.

We proposed latent framework to enable soft object representation more generic (independent from the object’s geometry and its mechanical properties) and scalable (it can work with 1D/2D/3D tasks). I

We present a robotic system to automatically perform defect inspection tasks over free-form specular surfaces, which the image acquisition sub-system is equipped with a 6-DOF robot manipulator to achieve flexible scanning.

we propose a new action planning approach to automatically pack long linear elastic objects into common-size boxes with a bimanual robotic system.

To achieve symbiotic human-robot interaction (HRI), this work proposes a mutual-cognitive safe HRI approach including worker visual augmentation, robot velocity control, Digital Twin-enabled motion preview and collision detection, and Deep Reinforcement Learning-based robot collision avoidance motion planning in the Augmented Reality-assisted manner.

Stolen from Jon Barron