1 citations · 2 across the 12 of their papers we have counts for
14 papers
Native Video-Action Pretraining for Generalizable Robot Control
Qihang Zhang, Lin Li, Luyao Zhang +26
The advent of video-action models offers a promising path for robot control. Nevertheless, we argue that repurposing video generative models designed for digital content creation i…
A Closed-Loop Multi-Agent Framework for Robust Multi-Robot Manipulation
Yi-Xiang He, Lan Wei, Haoming Cen +6
Multi-robot systems provide the parallelism and redundancy necessary for long-horizon tasks, while Large Language Models (LLMs) offer the reasoning capabilities to decompose these…
BrickCraft: Visuomotor Skill Composition with Situated Manual Guidance for Long-Horizon Interlocking Brick Assembly
Jichuan Yu, Bowei Li, Zhenran Tang +4
Autonomous robotic assembly of interlocking bricks demands seamless integration of long-horizon task reasoning, spatial grounding, and fine-grained manipulation. This paper present…
Human-in-the-loop Online Rejection Sampling for Robotic Manipulation
Guanxing Lu, Rui Zhao, Haitao Lin +2
Reinforcement learning (RL) is widely used to produce robust robotic manipulation policies, but fine-tuning vision-language-action (VLA) models with RL can be unstable due to inacc…
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…
ThinkBot: Embodied Instruction Following with Thought Chain Reasoning
Guanxing Lu, Ziwei Wang, Changliu Liu +2
Embodied Instruction Following (EIF) requires agents to complete human instruction by interacting objects in complicated surrounding environments. Conventional methods directly con…