Ziwei Wang

SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation

Changyuan Wang, Chubin Zhang, Zhenyu Wu +8

Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks. How…

WorldSample: Closed-loop Real-robot RL with World Modelling

Yuquan Xue, Le Xu, Zeyi Liu +5

Reinforcement learning (RL) can overcome the demonstration-coverage limitation of imitation learning (IL) by allowing robots to improve through trial-and-error interaction beyond t…

DVG-WM: Disentangled Video Generation Enables Efficient Embodied World Model for Robotic Manipulation

Ziyu Shan, Zhenyu Wu, Xiaofeng Wang +2

Video-based embodied world models provide an appealing substrate for robotic manipulation by predicting future states, yet current approaches remain limited by a fundamental entang…

RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation

Haichao Liu, Yuheng Zhou, Zhenyu Wu +14

Embodied Artificial Intelligence (EAI) is rapidly developing, gradually subverting previous autonomous systems' paradigms from isolated perception to integrated, continuous action.…

RESample: A Robust Data Augmentation Framework via Exploratory Sampling for Robotic Manipulation

Yuquan Xue, Guanxing Lu, Zhenyu Wu +4

Vision-Language-Action (VLA) models have shown strong manipulation capability when trained with large-scale imitation learning datasets. However, these datasets that predominantly…

RoDyn: Taming Interactive Robot-Dynamic 2.5D World Model for Robotic Manipulation

Chuanrui Zhang, Zhengxian Wu, Guanxing Lu +2

Learned world models hold significant potential as neural simulators for robotic manipulation. However, prevalent 2D video-based models inherently lack the spatial and kinematic re…

GWM: Towards Scalable Gaussian World Models for Robotic Manipulation

Guanxing Lu, Baoxiong Jia, Puhao Li +4

Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have show…

VLA-Reasoner: Empowering Vision-Language-Action Models with Reasoning via Online Monte Carlo Tree Search

Wenkai Guo, Guanxing Lu, Haoyuan Deng +3

Vision-Language-Action models (VLAs) achieve strong performance in general robotic manipulation tasks by scaling imitation learning. However, existing VLAs are limited to predictin…

ManiGaussian++: General Robotic Bimanual Manipulation with Hierarchical Gaussian World Model

Tengbo Yu, Guanxing Lu, Zaijia Yang +7

Multi-task robotic bimanual manipulation is becoming increasingly popular as it enables sophisticated tasks that require diverse dual-arm collaboration patterns. Compared to uniman…

VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning

Guanxing Lu, Wenkai Guo, Chubin Zhang +5

Recent high-capacity vision-language-action (VLA) models have demonstrated impressive performance on a range of robotic manipulation tasks by imitating human demonstrations. Howeve…

AnyBimanual: Transferring Unimanual Policy for General Bimanual Manipulation

Guanxing Lu, Tengbo Yu, Haoyuan Deng +3

Performing general language-conditioned bimanual manipulation tasks is of great importance for many applications ranging from household service to industrial assembly. However, col…

ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation

Zifeng Gao, Guanxing Lu, Tianxing Chen +5

Diffusion models have been verified to be effective in generating complex distributions from natural images to motion trajectories. Recent diffusion-based methods show impressive p…

ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation

Guanxing Lu, Shiyi Zhang, Ziwei Wang +3

Performing language-conditioned robotic manipulation tasks in unstructured environments is highly demanded for general intelligent robots. Conventional robotic manipulation methods…