Zhenliang Zhang

Research Scientist of AI

Beijing Institute for General Artificial Intelligence

Biography

Dr. Zhang is a full-time research scientist at Beijing Institute for General Artificial Intelligence (BIGAI). He received his Ph.D. degree (‘19) from Beijing Institute of Technology and also worked as a visiting Ph.D. student at the Center for Vision, Cognition, Learning, and Autonomy (VCLA) of UCLA. His research interest is currently focused on reinforcement learning, embodied artificial intelligence, autonomous robots, and symmetrical reality.

Open PhD Positions for 2026 Fall: (Update April 27, 2025) I am seeking outstanding candidates for the Joint PhD Program with Beijing Institute of Technology, Beihang University, Beijing Normal University, Zhejiang University, and many other leading institutes. If you have a strong academic background and would like to join our team through this doctoral program, please feel free to contact me! (zlzhang@bigai.ai)

I apologize that I may not be able to reply to every message, but please believe that your excellence will ultimately make you stand out!

Interests

Artificial General Intelligence
Autonomous Robots
Human-Computer Interaction
Mixed/Virtual Reality
Symmetrical Reality

Education

PhD in Optical Engineering, 2019

Beijing Institute of Technology
Joint PhD in Statistics, 2019

University of California, Los Angeles
BEng in Electronic Science&Technology, 2013

Beijing Institute of Technology

Recent Publications

Quickly discover relevant content by filtering publications.

[TOMM 2024] Demonstrative Learning for Human-Agent Knowledge Transfer

We propose a comprehensive system that combines the SDL paradigm with the TDL paradigm in VR from a top-down perspective.

Xiaonuo Dongye, Haiyan Jiang, Dongdong Weng, Zhenliang Zhang

[TOMM 2024] Demonstrative Learning for Human-Agent Knowledge Transfer

[MM 2024] In Situ 3D Scene Synthesis for Ubiquitous Embodied Interfaces

In this paper, we propose a scene agent that synthesizes situated 3D virtual scenes as a kind of ubiquitous embodied interface in VR for users. The scene agent synthesizes scenes by perceiving the user’s physical environment as well as inferring the user’s demands.

Haiyan Jiang, Leiyu Song, Dongdong Weng, Zhe Sun, Huiying Li, Xiaonuo Dongye, Zhenliang Zhang

[MM 2024] In Situ 3D Scene Synthesis for Ubiquitous Embodied Interfaces

[ICLR 2024] Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World

In this paper, We introduce Bongard-OpenWorld, a new benchmark for evaluating real-world few-shot reasoning for machine vision.

Rujie Wu, Xiaojian Ma, Zhenliang Zhang, Wei Wang, Qing Li, Song-Chun Zhu, Yizhou Wang

[ICLR 2024] Bongard-OpenWorld: Few-Shot Reasoning for Free-form Visual Concepts in the Real World

[VR 2024] On the Emergence of Symmetrical Reality

In this paper, we introduce the symmetrical reality framework, which offers a unified representation encompassing various forms of physical-virtual amalgamations.

Zhenliang Zhang, Zeyu Zhang, Ziyuan Jiao, Yao Su, Hangxin Liu, Wei Wang, Song-Chun Zhu

[VR 2024] On the Emergence of Symmetrical Reality

[TOG/SiggraphAsia 2023] Commonsense Knowledge-Driven Joint Reasoning Approach for Object Retrieval in Virtual Reality

we propose a commonsense knowledge-driven joint reasoning approach for object retrieval, where human grasping gestures and context are modeled using an And-Or graph (AOG).

Haiyan Jiang, Dongdong Weng, Xiaonuo Dongye, Le Luo, Zhenliang Zhang

[TOG/SiggraphAsia 2023] Commonsense Knowledge-Driven Joint Reasoning Approach for Object Retrieval in Virtual Reality

[UIST 2023] Neighbor-Environment Observer: An Intelligent Agent for Immersive Working Companionship

In this paper, we propose a joint observation strategy for artificial agents to support users across virtual and physical environments.

Zhe Sun, Qixuan Liang, Meng Wang, Zhenliang Zhang

[UIST 2023] Neighbor-Environment Observer: An Intelligent Agent for Immersive Working Companionship

[Engineering 2023] The tong test: Evaluating artificial general intelligence through dynamic embodied physical and social interactions

The Tong test describes a value- and ability-oriented testing system that delineates five levels of AGI milestones through a virtual environment with DEPSI, allowing for infinite task generation.

Yujia Peng, Jiaheng Han, Zhenliang Zhang, Lifeng Fan, Tengyu Liu, Siyuan Qi, Xue Feng, Yuxi Ma, Yizhou Wang, Song-Chun Zhu

[Engineering 2023] The tong test: Evaluating artificial general intelligence through dynamic embodied physical and social interactions

[Virtual Reality 2023] DexHand: dexterous hand manipulation motion synthesis for virtual reality

We propose a neural network-based finger movement generation approach, enabling the generation of plausible hand motions interacting with objects.

Haiyan Jiang, Dongdong Weng, Zhen Song, Xiaonuo Dongye, Zhenliang Zhang

[Virtual Reality 2023] DexHand: dexterous hand manipulation motion synthesis for virtual reality

[VR 2023] Building symmetrical reality systems for cooperative manipulation

In this paper, we discuss the symmetrical reality-based human-robot interaction and show the general pipeline of building the symmetrical reality system with two minds.

Zhenliang Zhang

[VR 2023] Building symmetrical reality systems for cooperative manipulation

[Engineering 2023] A reconfigurable data glove for reconstructing physical and virtual grasps

We present a reconfigurable data glove design to capture different modes of human hand-object interactions, which are critical in training embodied artificial intelligence (AI) agents for fine manipulation tasks.

Hangxin Liu, Zeyu Zhang, Ziyuan Jiao, Zhenliang Zhang, Mingchen Li, Chenfanfu Jiang, Yixin Zhu, Song-Chun Zhu

[Engineering 2023] A reconfigurable data glove for reconstructing physical and virtual grasps

See all publications

Gallery

Contact

Please feel free to contact me at office hours.

zzlyw10@gmail.com
2 Summer Palace Road, Beijing 100080
Weekday 09:00 to 18:00
Weekend 12:00 to 16:00