6 citations · 19 across the 14 of their papers we have counts for
14 papers
SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Design
Ruogu Li, Sikai Li, Yao Mu +1
We introduce SldprtNet, a large-scale dataset comprising over 242,000 industrial parts, designed for semantic-driven CAD modeling, geometric deep learning, and the training and fin…
AutoBio: A Simulation and Benchmark for Robotic Automation in Digital Biology Laboratory
Zhiqian Lan, Yuxuan Jiang, Ruiqi Wang +12
Vision-language-action (VLA) models have shown promise as generalist robotic policies by jointly leveraging visual, linguistic, and proprioceptive modalities to generate action tra…
RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins
Yao Mu, Tianxing Chen, Zanxin Chen +11
In the rapidly advancing field of robotics, dual-arm coordination and complex object manipulation are essential capabilities for developing advanced autonomous systems. However, th…
Text2World: Benchmarking Large Language Models for Symbolic World Model Generation
Mengkang Hu, Tianxing Chen, Yude Zou +7
Recently, there has been growing interest in leveraging large language models (LLMs) to generate symbolic world models from textual descriptions. Although LLMs have been extensivel…
DexHandDiff: Interaction-aware Diffusion Planning for Adaptive Dexterous Manipulation
Zhixuan Liang, Yao Mu, Yixiao Wang +6
Dexterous manipulation with contact-rich interactions is crucial for advanced robotics. While recent diffusion-based planning approaches show promise for simple manipulation tasks,…
G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation
Tianxing Chen, Yao Mu, Zhixuan Liang +8
Recent advances in imitation learning for 3D robotic manipulation have shown promising results with diffusion-based policies. However, achieving human-level dexterity requires seam…
Autoregressive Models in Vision: A Survey
Jing Xiong, Gongye Liu, Lun Huang +17
Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in co…
RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins (early version)
Yao Mu, Tianxing Chen, Shijia Peng +6
In the rapidly advancing field of robotics, dual-arm coordination and complex object manipulation are essential capabilities for developing advanced autonomous systems. However, th…
HiAgent: Hierarchical Working Memory Management for Solving Long-Horizon Agent Tasks with Large Language Model
Mengkang Hu, Tianxing Chen, Qiguang Chen +3
Large Language Model (LLM)-based agents exhibit significant potential across various domains, operating as interactive systems that process environmental observations to generate e…
DAG-Plan: Generating Directed Acyclic Dependency Graphs for Dual-Arm Cooperative Planning
Zeyu Gao, Yao Mu, Jinye Qu +7
Dual-arm robots promise greater efficiency but require planning for complex tasks with nonlinear sub-task dependencies. Current methods using Large Language Models (LLMs) suffer fr…
RoboCodeX: Multimodal Code Generation for Robotic Behavior Synthesis
Yao Mu, Junting Chen, Qinglong Zhang +16
Robotic behavior synthesis, the problem of understanding multimodal inputs and generating precise physical control for robots, is an important part of Embodied AI. Despite successe…
RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
Junting Chen, Yao Mu, Qiaojun Yu +11
Rapid progress in high-level task planning and code generation for open-world robot manipulation has been witnessed in Embodied AI. However, previous studies put much effort into g…
SkillDiffuser: Interpretable Hierarchical Planning via Skill Abstractions in Diffusion-Based Task Execution
Zhixuan Liang, Yao Mu, Hengbo Ma +3
Diffusion models have demonstrated strong potential for robotic trajectory planning. However, generating coherent trajectories from high-level instructions remains challenging, esp…
Tree-Planner: Efficient Close-loop Task Planning with Large Language Models
Mengkang Hu, Yao Mu, Xinmiao Yu +7
This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on…